##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(tidyr)
library(caret)
library(lattice)
library(dplyr)
library(moments)
library(skimr)
library(RANN)
library(pls)
library(corrplot)
library(lares)
library(DMwR)
library(gridExtra)
library(rattle)
library(RColorBrewer)
library(stats)
library(caretEnsemble)
library(pROC)
library(adabag)
library(gbm)
library(xgboost)
##################################
# Loading source and
# formulating the analysis set
##################################
BreastCancer <- read.csv("WisconsinBreastCancer.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
BreastCancer <- as.data.frame(BreastCancer)
##################################
# Performing a general exploration of the data set
##################################
dim(BreastCancer)## [1] 1138 32
str(BreastCancer)## 'data.frame': 1138 obs. of 32 variables:
## $ id : int 842302 842517 84300903 84348301 84358402 843786 844359 84458202 844981 84501001 ...
## $ diagnosis : chr "M" "M" "M" "M" ...
## $ radius_mean : num 18 20.6 19.7 11.4 20.3 ...
## $ texture_mean : num 10.4 17.8 21.2 20.4 14.3 ...
## $ perimeter_mean : num 122.8 132.9 130 77.6 135.1 ...
## $ area_mean : num 1001 1326 1203 386 1297 ...
## $ smoothness_mean : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## $ compactness_mean : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## $ concavity_mean : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
## $ concave.points_mean : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## $ symmetry_mean : num 0.242 0.181 0.207 0.26 0.181 ...
## $ fractal_dimension_mean : num 0.0787 0.0567 0.06 0.0974 0.0588 ...
## $ radius_se : num 1.095 0.543 0.746 0.496 0.757 ...
## $ texture_se : num 0.905 0.734 0.787 1.156 0.781 ...
## $ perimeter_se : num 8.59 3.4 4.58 3.44 5.44 ...
## $ area_se : num 153.4 74.1 94 27.2 94.4 ...
## $ smoothness_se : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## $ compactness_se : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
## $ concavity_se : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## $ concave.points_se : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## $ symmetry_se : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
## $ fractal_dimension_se : num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## $ radius_worst : num 25.4 25 23.6 14.9 22.5 ...
## $ texture_worst : num 17.3 23.4 25.5 26.5 16.7 ...
## $ perimeter_worst : num 184.6 158.8 152.5 98.9 152.2 ...
## $ area_worst : num 2019 1956 1709 568 1575 ...
## $ smoothness_worst : num 0.162 0.124 0.144 0.21 0.137 ...
## $ compactness_worst : num 0.666 0.187 0.424 0.866 0.205 ...
## $ concavity_worst : num 0.712 0.242 0.45 0.687 0.4 ...
## $ concave.points_worst : num 0.265 0.186 0.243 0.258 0.163 ...
## $ symmetry_worst : num 0.46 0.275 0.361 0.664 0.236 ...
## $ fractal_dimension_worst: num 0.1189 0.089 0.0876 0.173 0.0768 ...
summary(BreastCancer)## id diagnosis radius_mean texture_mean
## Min. : 8670 Length:1138 Min. : 6.981 Min. : 9.71
## 1st Qu.: 869218 Class :character 1st Qu.:11.700 1st Qu.:16.17
## Median : 906024 Mode :character Median :13.370 Median :18.84
## Mean : 30371831 Mean :14.127 Mean :19.29
## 3rd Qu.: 8813129 3rd Qu.:15.780 3rd Qu.:21.80
## Max. :911320502 Max. :28.110 Max. :39.28
## perimeter_mean area_mean smoothness_mean compactness_mean
## Min. : 43.79 Min. : 143.5 Min. :0.05263 Min. :0.01938
## 1st Qu.: 75.17 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492
## Median : 86.24 Median : 551.1 Median :0.09587 Median :0.09263
## Mean : 91.97 Mean : 654.9 Mean :0.09636 Mean :0.10434
## 3rd Qu.:104.10 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040
## Max. :188.50 Max. :2501.0 Max. :0.16340 Max. :0.34540
## concavity_mean concave.points_mean symmetry_mean fractal_dimension_mean
## Min. :0.00000 Min. :0.00000 Min. :0.1060 Min. :0.04996
## 1st Qu.:0.02956 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770
## Median :0.06154 Median :0.03350 Median :0.1792 Median :0.06154
## Mean :0.08880 Mean :0.04892 Mean :0.1812 Mean :0.06280
## 3rd Qu.:0.13070 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612
## Max. :0.42680 Max. :0.20120 Max. :0.3040 Max. :0.09744
## radius_se texture_se perimeter_se area_se
## Min. :0.1115 Min. :0.3602 Min. : 0.757 Min. : 6.802
## 1st Qu.:0.2324 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850
## Median :0.3242 Median :1.1080 Median : 2.287 Median : 24.530
## Mean :0.4052 Mean :1.2169 Mean : 2.866 Mean : 40.337
## 3rd Qu.:0.4789 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190
## Max. :2.8730 Max. :4.8850 Max. :21.980 Max. :542.200
## smoothness_se compactness_se concavity_se concave.points_se
## Min. :0.001713 Min. :0.002252 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.005169 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638
## Median :0.006380 Median :0.020450 Median :0.02589 Median :0.010930
## Mean :0.007041 Mean :0.025478 Mean :0.03189 Mean :0.011796
## 3rd Qu.:0.008146 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710
## Max. :0.031130 Max. :0.135400 Max. :0.39600 Max. :0.052790
## symmetry_se fractal_dimension_se radius_worst texture_worst
## Min. :0.007882 Min. :0.0008948 Min. : 7.93 Min. :12.02
## 1st Qu.:0.015160 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08
## Median :0.018730 Median :0.0031870 Median :14.97 Median :25.41
## Mean :0.020542 Mean :0.0037949 Mean :16.27 Mean :25.68
## 3rd Qu.:0.023480 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72
## Max. :0.078950 Max. :0.0298400 Max. :36.04 Max. :49.54
## perimeter_worst area_worst smoothness_worst compactness_worst
## Min. : 50.41 Min. : 185.2 Min. :0.07117 Min. :0.02729
## 1st Qu.: 84.11 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720
## Median : 97.66 Median : 686.5 Median :0.13130 Median :0.21190
## Mean :107.26 Mean : 880.6 Mean :0.13237 Mean :0.25427
## 3rd Qu.:125.40 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910
## Max. :251.20 Max. :4254.0 Max. :0.22260 Max. :1.05800
## concavity_worst concave.points_worst symmetry_worst fractal_dimension_worst
## Min. :0.0000 Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.1145 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.2267 Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.2722 Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.3829 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :1.2520 Max. :0.29100 Max. :0.6638 Max. :0.20750
##################################
# Setting the data type
# for the response variable
##################################
BreastCancer$diagnosis <- factor(BreastCancer$diagnosis,
levels = c("M","B"))
##################################
# Formulating a data type assessment summary
##################################
PDA <- BreastCancer
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 id integer
## 2 2 diagnosis factor
## 3 3 radius_mean numeric
## 4 4 texture_mean numeric
## 5 5 perimeter_mean numeric
## 6 6 area_mean numeric
## 7 7 smoothness_mean numeric
## 8 8 compactness_mean numeric
## 9 9 concavity_mean numeric
## 10 10 concave.points_mean numeric
## 11 11 symmetry_mean numeric
## 12 12 fractal_dimension_mean numeric
## 13 13 radius_se numeric
## 14 14 texture_se numeric
## 15 15 perimeter_se numeric
## 16 16 area_se numeric
## 17 17 smoothness_se numeric
## 18 18 compactness_se numeric
## 19 19 concavity_se numeric
## 20 20 concave.points_se numeric
## 21 21 symmetry_se numeric
## 22 22 fractal_dimension_se numeric
## 23 23 radius_worst numeric
## 24 24 texture_worst numeric
## 25 25 perimeter_worst numeric
## 26 26 area_worst numeric
## 27 27 smoothness_worst numeric
## 28 28 compactness_worst numeric
## 29 29 concavity_worst numeric
## 30 30 concave.points_worst numeric
## 31 31 symmetry_worst numeric
## 32 32 fractal_dimension_worst numeric
##################################
# Loading dataset
##################################
DQA <- BreastCancer
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 id integer 1138 0 1.000
## 2 diagnosis factor 1138 0 1.000
## 3 radius_mean numeric 1138 0 1.000
## 4 texture_mean numeric 1138 0 1.000
## 5 perimeter_mean numeric 1138 0 1.000
## 6 area_mean numeric 1138 0 1.000
## 7 smoothness_mean numeric 1138 0 1.000
## 8 compactness_mean numeric 1138 0 1.000
## 9 concavity_mean numeric 1138 0 1.000
## 10 concave.points_mean numeric 1138 0 1.000
## 11 symmetry_mean numeric 1138 0 1.000
## 12 fractal_dimension_mean numeric 1138 0 1.000
## 13 radius_se numeric 1138 0 1.000
## 14 texture_se numeric 1138 0 1.000
## 15 perimeter_se numeric 1138 0 1.000
## 16 area_se numeric 1138 0 1.000
## 17 smoothness_se numeric 1138 0 1.000
## 18 compactness_se numeric 1138 0 1.000
## 19 concavity_se numeric 1138 0 1.000
## 20 concave.points_se numeric 1138 0 1.000
## 21 symmetry_se numeric 1138 0 1.000
## 22 fractal_dimension_se numeric 1138 0 1.000
## 23 radius_worst numeric 1138 0 1.000
## 24 texture_worst numeric 1138 0 1.000
## 25 perimeter_worst numeric 1138 0 1.000
## 26 area_worst numeric 1138 0 1.000
## 27 smoothness_worst numeric 1138 0 1.000
## 28 compactness_worst numeric 1138 0 1.000
## 29 concavity_worst numeric 1138 0 1.000
## 30 concave.points_worst numeric 1138 0 1.000
## 31 symmetry_worst numeric 1138 0 1.000
## 32 fractal_dimension_worst numeric 1138 0 1.000
##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("id","diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric)]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 30 numeric predictor variable(s)."
##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor)]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are no factor predictor variables."
##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 1 radius_mean numeric 456 0.401
## 2 texture_mean numeric 479 0.421
## 3 perimeter_mean numeric 522 0.459
## 4 area_mean numeric 539 0.474
## 5 smoothness_mean numeric 474 0.417
## 6 compactness_mean numeric 537 0.472
## 7 concavity_mean numeric 537 0.472
## 8 concave.points_mean numeric 542 0.476
## 9 symmetry_mean numeric 432 0.380
## 10 fractal_dimension_mean numeric 499 0.438
## 11 radius_se numeric 540 0.475
## 12 texture_se numeric 519 0.456
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 15 smoothness_se numeric 547 0.481
## 16 compactness_se numeric 541 0.475
## 17 concavity_se numeric 533 0.468
## 18 concave.points_se numeric 507 0.446
## 19 symmetry_se numeric 498 0.438
## 20 fractal_dimension_se numeric 545 0.479
## 21 radius_worst numeric 457 0.402
## 22 texture_worst numeric 511 0.449
## 23 perimeter_worst numeric 514 0.452
## 24 area_worst numeric 544 0.478
## 25 smoothness_worst numeric 411 0.361
## 26 compactness_worst numeric 529 0.465
## 27 concavity_worst numeric 539 0.474
## 28 concave.points_worst numeric 492 0.432
## 29 symmetry_worst numeric 500 0.439
## 30 fractal_dimension_worst numeric 535 0.470
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 1 12.340 13.000 8 6
## 2 15.700 21.250 6 4
## 3 82.610 132.900 6 4
## 4 512.200 658.800 6 4
## 5 0.101 0.108 10 8
## 6 0.121 0.160 6 4
## 7 0.000 0.120 26 6
## 8 0.000 0.029 26 6
## 9 0.177 0.181 8 6
## 10 0.057 0.059 6 4
## 11 0.286 0.298 6 4
## 12 1.150 0.734 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 15 0.006 0.005 4 2
## 16 0.023 0.014 6 4
## 17 0.000 0.017 26 4
## 18 0.000 0.012 26 6
## 19 0.013 0.015 8 6
## 20 0.003 0.006 4 2
## 21 12.360 13.340 10 8
## 22 27.260 27.660 6 4
## 23 117.700 184.600 6 4
## 24 1269.000 2019.000 4 2
## 25 0.131 0.149 8 6
## 26 0.342 0.177 6 4
## 27 0.000 0.450 26 6
## 28 0.000 0.026 26 6
## 29 0.320 0.361 6 4
## 30 0.074 0.084 6 4
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 1 1.333 6.981 14.127 13.370 28.110 0.940 3.828
## 2 1.500 9.710 19.290 18.840 39.280 0.649 3.741
## 3 1.500 43.790 91.969 86.240 188.500 0.988 3.953
## 4 1.500 143.500 654.889 551.100 2501.000 1.641 6.610
## 5 1.250 0.053 0.096 0.096 0.163 0.455 3.838
## 6 1.500 0.019 0.104 0.093 0.345 1.187 4.625
## 7 4.333 0.000 0.089 0.062 0.427 1.397 4.971
## 8 4.333 0.000 0.049 0.034 0.201 1.168 4.047
## 9 1.333 0.106 0.181 0.179 0.304 0.724 4.266
## 10 1.500 0.050 0.063 0.062 0.097 1.301 5.969
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 12 1.500 0.360 1.217 1.108 4.885 1.642 8.292
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 15 2.000 0.002 0.007 0.006 0.031 2.308 13.368
## 16 1.500 0.002 0.025 0.020 0.135 1.897 8.051
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 18 4.333 0.000 0.012 0.011 0.053 1.441 8.071
## 19 1.333 0.008 0.021 0.019 0.079 2.189 10.816
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## 21 1.250 7.930 16.269 14.970 36.040 1.100 3.925
## 22 1.500 12.020 25.677 25.410 49.540 0.497 3.212
## 23 1.500 50.410 107.261 97.660 251.200 1.125 4.050
## 24 2.000 185.200 880.583 686.500 4254.000 1.854 7.347
## 25 1.333 0.071 0.132 0.131 0.223 0.414 3.503
## 26 1.500 0.027 0.254 0.212 1.058 1.470 6.002
## 27 4.333 0.000 0.272 0.227 1.252 1.147 4.591
## 28 4.333 0.000 0.115 0.100 0.291 0.491 2.459
## 29 1.500 0.156 0.290 0.282 0.664 1.430 7.395
## 30 1.500 0.055 0.084 0.080 0.208 1.658 8.188
## Percentile25th Percentile75th
## 1 11.700 15.780
## 2 16.170 21.800
## 3 75.170 104.100
## 4 420.300 782.700
## 5 0.086 0.105
## 6 0.065 0.130
## 7 0.030 0.131
## 8 0.020 0.074
## 9 0.162 0.196
## 10 0.058 0.066
## 11 0.232 0.479
## 12 0.834 1.474
## 13 1.606 3.357
## 14 17.850 45.190
## 15 0.005 0.008
## 16 0.013 0.032
## 17 0.015 0.042
## 18 0.008 0.015
## 19 0.015 0.023
## 20 0.002 0.005
## 21 13.010 18.790
## 22 21.080 29.720
## 23 84.110 125.400
## 24 515.300 1084.000
## 25 0.117 0.146
## 26 0.147 0.339
## 27 0.114 0.383
## 28 0.065 0.161
## 29 0.250 0.318
## 30 0.071 0.092
##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."
##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "No factor predictors noted."
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>5."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 17 concavity_se numeric 533 0.468 0.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 17 0.017 26 4 6.500
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 17 0.000 0.032 0.026 0.396 5.097 51.423 0.015 0.042
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric predictors due to low unique count ratio noted."
##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric predictors noted.")
}## [1] "High skewness observed for 5 numeric variable(s) with Skewness>3 or Skewness<(-3)."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 11 radius_se numeric 540 0.475
## 13 perimeter_se numeric 533 0.468
## 14 area_se numeric 528 0.464
## 17 concavity_se numeric 533 0.468
## 20 fractal_dimension_se numeric 545 0.479
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 11 0.286 0.298 6 4
## 13 1.778 2.406 8 4
## 14 16.970 74.080 6 4
## 17 0.000 0.017 26 4
## 20 0.003 0.006 4 2
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 11 1.500 0.112 0.405 0.324 2.873 3.080 20.521
## 13 2.000 0.757 2.866 2.287 21.980 3.435 24.204
## 14 1.500 6.802 40.337 24.530 542.200 5.433 51.767
## 17 6.500 0.000 0.032 0.026 0.396 5.097 51.423
## 20 2.000 0.001 0.004 0.003 0.030 3.914 29.040
## Percentile25th Percentile75th
## 11 0.232 0.479
## 13 1.606 3.357
## 14 17.850 45.190
## 17 0.015 0.042
## 20 0.002 0.005
##################################
# Loading dataset
##################################
DPA <- DQA[,!names(DQA) %in% c("id")]
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA)) | Name | DPA |
| Number of rows | 1138 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 30 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| radius_mean | 0 | 1 | 14.13 | 3.52 | 6.98 | 11.70 | 13.37 | 15.78 | 28.11 | ▂▇▃▁▁ |
| texture_mean | 0 | 1 | 19.29 | 4.30 | 9.71 | 16.17 | 18.84 | 21.80 | 39.28 | ▃▇▃▁▁ |
| perimeter_mean | 0 | 1 | 91.97 | 24.29 | 43.79 | 75.17 | 86.24 | 104.10 | 188.50 | ▃▇▃▁▁ |
| area_mean | 0 | 1 | 654.89 | 351.76 | 143.50 | 420.30 | 551.10 | 782.70 | 2501.00 | ▇▃▂▁▁ |
| smoothness_mean | 0 | 1 | 0.10 | 0.01 | 0.05 | 0.09 | 0.10 | 0.11 | 0.16 | ▁▇▇▁▁ |
| compactness_mean | 0 | 1 | 0.10 | 0.05 | 0.02 | 0.06 | 0.09 | 0.13 | 0.35 | ▇▇▂▁▁ |
| concavity_mean | 0 | 1 | 0.09 | 0.08 | 0.00 | 0.03 | 0.06 | 0.13 | 0.43 | ▇▃▂▁▁ |
| concave.points_mean | 0 | 1 | 0.05 | 0.04 | 0.00 | 0.02 | 0.03 | 0.07 | 0.20 | ▇▃▂▁▁ |
| symmetry_mean | 0 | 1 | 0.18 | 0.03 | 0.11 | 0.16 | 0.18 | 0.20 | 0.30 | ▁▇▅▁▁ |
| fractal_dimension_mean | 0 | 1 | 0.06 | 0.01 | 0.05 | 0.06 | 0.06 | 0.07 | 0.10 | ▆▇▂▁▁ |
| radius_se | 0 | 1 | 0.41 | 0.28 | 0.11 | 0.23 | 0.32 | 0.48 | 2.87 | ▇▁▁▁▁ |
| texture_se | 0 | 1 | 1.22 | 0.55 | 0.36 | 0.83 | 1.11 | 1.47 | 4.88 | ▇▅▁▁▁ |
| perimeter_se | 0 | 1 | 2.87 | 2.02 | 0.76 | 1.61 | 2.29 | 3.36 | 21.98 | ▇▁▁▁▁ |
| area_se | 0 | 1 | 40.34 | 45.47 | 6.80 | 17.85 | 24.53 | 45.19 | 542.20 | ▇▁▁▁▁ |
| smoothness_se | 0 | 1 | 0.01 | 0.00 | 0.00 | 0.01 | 0.01 | 0.01 | 0.03 | ▇▃▁▁▁ |
| compactness_se | 0 | 1 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.03 | 0.14 | ▇▃▁▁▁ |
| concavity_se | 0 | 1 | 0.03 | 0.03 | 0.00 | 0.02 | 0.03 | 0.04 | 0.40 | ▇▁▁▁▁ |
| concave.points_se | 0 | 1 | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.01 | 0.05 | ▇▇▁▁▁ |
| symmetry_se | 0 | 1 | 0.02 | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | 0.08 | ▇▃▁▁▁ |
| fractal_dimension_se | 0 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | ▇▁▁▁▁ |
| radius_worst | 0 | 1 | 16.27 | 4.83 | 7.93 | 13.01 | 14.97 | 18.79 | 36.04 | ▆▇▃▁▁ |
| texture_worst | 0 | 1 | 25.68 | 6.14 | 12.02 | 21.08 | 25.41 | 29.72 | 49.54 | ▃▇▆▁▁ |
| perimeter_worst | 0 | 1 | 107.26 | 33.59 | 50.41 | 84.11 | 97.66 | 125.40 | 251.20 | ▇▇▃▁▁ |
| area_worst | 0 | 1 | 880.58 | 569.11 | 185.20 | 515.30 | 686.50 | 1084.00 | 4254.00 | ▇▂▁▁▁ |
| smoothness_worst | 0 | 1 | 0.13 | 0.02 | 0.07 | 0.12 | 0.13 | 0.15 | 0.22 | ▂▇▇▂▁ |
| compactness_worst | 0 | 1 | 0.25 | 0.16 | 0.03 | 0.15 | 0.21 | 0.34 | 1.06 | ▇▅▁▁▁ |
| concavity_worst | 0 | 1 | 0.27 | 0.21 | 0.00 | 0.11 | 0.23 | 0.38 | 1.25 | ▇▅▂▁▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | 0.29 | 0.06 | 0.16 | 0.25 | 0.28 | 0.32 | 0.66 | ▅▇▁▁▁ |
| fractal_dimension_worst | 0 | 1 | 0.08 | 0.02 | 0.06 | 0.07 | 0.08 | 0.09 | 0.21 | ▇▃▁▁▁ |
##################################
# Outlier Detection
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric Predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric Predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## [1] freqRatio percentUnique zeroVar nzv
## <0 rows> (or 0-length row.names)
if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance descriptors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "No low variance descriptors noted."
##################################
# Visualizing pairwise correlation between Predictor
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## radius_mean texture_mean perimeter_mean area_mean
## radius_mean 1.000000000 0.323781891 0.997855281 0.987357170
## texture_mean 0.323781891 1.000000000 0.329533059 0.321085696
## perimeter_mean 0.997855281 0.329533059 1.000000000 0.986506804
## area_mean 0.987357170 0.321085696 0.986506804 1.000000000
## smoothness_mean 0.170581187 -0.023388516 0.207278164 0.177028377
## compactness_mean 0.506123578 0.236702222 0.556936211 0.498501682
## concavity_mean 0.676763550 0.302417828 0.716135650 0.685982829
## concave.points_mean 0.822528522 0.293464051 0.850977041 0.823268869
## symmetry_mean 0.147741242 0.071400980 0.183027212 0.151293079
## fractal_dimension_mean -0.311630826 -0.076437183 -0.261476908 -0.283109812
## radius_se 0.679090388 0.275868676 0.691765014 0.732562227
## texture_se -0.097317443 0.386357623 -0.086761078 -0.066280214
## perimeter_se 0.674171616 0.281673115 0.693134890 0.726628328
## area_se 0.735863663 0.259844987 0.744982694 0.800085921
## smoothness_se -0.222600125 0.006613777 -0.202694026 -0.166776667
## compactness_se 0.205999980 0.191974611 0.250743681 0.212582551
## concavity_se 0.194203623 0.143293077 0.228082345 0.207660060
## concave.points_se 0.376168956 0.163851025 0.407216916 0.372320282
## symmetry_se -0.104320881 0.009127168 -0.081629327 -0.072496588
## fractal_dimension_se -0.042641269 0.054457520 -0.005523391 -0.019886963
## radius_worst 0.969538973 0.352572947 0.969476363 0.962746086
## texture_worst 0.297007644 0.912044589 0.303038372 0.287488627
## perimeter_worst 0.965136514 0.358039575 0.970386887 0.959119574
## area_worst 0.941082460 0.343545947 0.941549808 0.959213326
## smoothness_worst 0.119616140 0.077503359 0.150549404 0.123522939
## compactness_worst 0.413462823 0.277829592 0.455774228 0.390410309
## concavity_worst 0.526911462 0.301025224 0.563879263 0.512605920
## concave.points_worst 0.744214198 0.295315843 0.771240789 0.722016626
## symmetry_worst 0.163953335 0.105007910 0.189115040 0.143569914
## fractal_dimension_worst 0.007065886 0.119205351 0.051018530 0.003737597
## smoothness_mean compactness_mean concavity_mean
## radius_mean 0.17058119 0.50612358 0.67676355
## texture_mean -0.02338852 0.23670222 0.30241783
## perimeter_mean 0.20727816 0.55693621 0.71613565
## area_mean 0.17702838 0.49850168 0.68598283
## smoothness_mean 1.00000000 0.65912322 0.52198377
## compactness_mean 0.65912322 1.00000000 0.88312067
## concavity_mean 0.52198377 0.88312067 1.00000000
## concave.points_mean 0.55369517 0.83113504 0.92139103
## symmetry_mean 0.55777479 0.60264105 0.50066662
## fractal_dimension_mean 0.58479200 0.56536866 0.33678336
## radius_se 0.30146710 0.49747345 0.63192482
## texture_se 0.06840645 0.04620483 0.07621835
## perimeter_se 0.29609193 0.54890526 0.66039079
## area_se 0.24655243 0.45565285 0.61742681
## smoothness_se 0.33237544 0.13529927 0.09856375
## compactness_se 0.31894330 0.73872179 0.67027882
## concavity_se 0.24839568 0.57051687 0.69127021
## concave.points_se 0.38067569 0.64226185 0.68325992
## symmetry_se 0.20077438 0.22997659 0.17800921
## fractal_dimension_se 0.28360670 0.50731813 0.44930075
## radius_worst 0.21312014 0.53531540 0.68823641
## texture_worst 0.03607180 0.24813283 0.29987889
## perimeter_worst 0.23885263 0.59021043 0.72956492
## area_worst 0.20671836 0.50960381 0.67598723
## smoothness_worst 0.80532420 0.56554117 0.44882204
## compactness_worst 0.47246844 0.86580904 0.75496802
## concavity_worst 0.43492571 0.81627525 0.88410264
## concave.points_worst 0.50305335 0.81557322 0.86132303
## symmetry_worst 0.39430948 0.51022343 0.40946413
## fractal_dimension_worst 0.49931637 0.68738232 0.51492989
## concave.points_mean symmetry_mean
## radius_mean 0.82252852 0.14774124
## texture_mean 0.29346405 0.07140098
## perimeter_mean 0.85097704 0.18302721
## area_mean 0.82326887 0.15129308
## smoothness_mean 0.55369517 0.55777479
## compactness_mean 0.83113504 0.60264105
## concavity_mean 0.92139103 0.50066662
## concave.points_mean 1.00000000 0.46249739
## symmetry_mean 0.46249739 1.00000000
## fractal_dimension_mean 0.16691738 0.47992133
## radius_se 0.69804983 0.30337926
## texture_se 0.02147958 0.12805293
## perimeter_se 0.71064987 0.31389276
## area_se 0.69029854 0.22397022
## smoothness_se 0.02765331 0.18732117
## compactness_se 0.49042425 0.42165915
## concavity_se 0.43916707 0.34262702
## concave.points_se 0.61563413 0.39329787
## symmetry_se 0.09535079 0.44913654
## fractal_dimension_se 0.25758375 0.33178615
## radius_worst 0.83031763 0.18572775
## texture_worst 0.29275171 0.09065069
## perimeter_worst 0.85592313 0.21916856
## area_worst 0.80962962 0.17719338
## smoothness_worst 0.45275305 0.42667503
## compactness_worst 0.66745368 0.47320001
## concavity_worst 0.75239950 0.43372101
## concave.points_worst 0.91015531 0.43029661
## symmetry_worst 0.37574415 0.69982580
## fractal_dimension_worst 0.36866113 0.43841350
## fractal_dimension_mean radius_se texture_se
## radius_mean -0.3116308263 0.6790903880 -0.09731744
## texture_mean -0.0764371834 0.2758686762 0.38635762
## perimeter_mean -0.2614769081 0.6917650135 -0.08676108
## area_mean -0.2831098117 0.7325622270 -0.06628021
## smoothness_mean 0.5847920019 0.3014670983 0.06840645
## compactness_mean 0.5653686634 0.4974734461 0.04620483
## concavity_mean 0.3367833594 0.6319248221 0.07621835
## concave.points_mean 0.1669173832 0.6980498336 0.02147958
## symmetry_mean 0.4799213301 0.3033792632 0.12805293
## fractal_dimension_mean 1.0000000000 0.0001109951 0.16417397
## radius_se 0.0001109951 1.0000000000 0.21324734
## texture_se 0.1641739659 0.2132473373 1.00000000
## perimeter_se 0.0398299316 0.9727936770 0.22317073
## area_se -0.0901702475 0.9518301121 0.11156725
## smoothness_se 0.4019644254 0.1645142198 0.39724285
## compactness_se 0.5598366906 0.3560645755 0.23169970
## concavity_se 0.4466303217 0.3323575376 0.19499846
## concave.points_se 0.3411980444 0.5133464414 0.23028340
## symmetry_se 0.3450073971 0.2405673625 0.41162068
## fractal_dimension_se 0.6881315775 0.2277535327 0.27972275
## radius_worst -0.2536914949 0.7150651951 -0.11169031
## texture_worst -0.0512692020 0.1947985568 0.40900277
## perimeter_worst -0.2051512113 0.7196838037 -0.10224192
## area_worst -0.2318544512 0.7515484761 -0.08319499
## smoothness_worst 0.5049420754 0.1419185529 -0.07365766
## compactness_worst 0.4587981567 0.2871031656 -0.09243935
## concavity_worst 0.3462338763 0.3805846346 -0.06895622
## concave.points_worst 0.1753254492 0.5310623278 -0.11963752
## symmetry_worst 0.3340186839 0.0945428304 -0.12821476
## fractal_dimension_worst 0.7672967792 0.0495594325 -0.04565457
## perimeter_se area_se smoothness_se compactness_se
## radius_mean 0.67417162 0.73586366 -0.222600125 0.2060000
## texture_mean 0.28167311 0.25984499 0.006613777 0.1919746
## perimeter_mean 0.69313489 0.74498269 -0.202694026 0.2507437
## area_mean 0.72662833 0.80008592 -0.166776667 0.2125826
## smoothness_mean 0.29609193 0.24655243 0.332375443 0.3189433
## compactness_mean 0.54890526 0.45565285 0.135299268 0.7387218
## concavity_mean 0.66039079 0.61742681 0.098563746 0.6702788
## concave.points_mean 0.71064987 0.69029854 0.027653308 0.4904242
## symmetry_mean 0.31389276 0.22397022 0.187321165 0.4216591
## fractal_dimension_mean 0.03982993 -0.09017025 0.401964425 0.5598367
## radius_se 0.97279368 0.95183011 0.164514220 0.3560646
## texture_se 0.22317073 0.11156725 0.397242853 0.2316997
## perimeter_se 1.00000000 0.93765541 0.151075331 0.4163224
## area_se 0.93765541 1.00000000 0.075150338 0.2848401
## smoothness_se 0.15107533 0.07515034 1.000000000 0.3366961
## compactness_se 0.41632237 0.28484006 0.336696081 1.0000000
## concavity_se 0.36248158 0.27089473 0.268684760 0.8012683
## concave.points_se 0.55626408 0.41572957 0.328429499 0.7440827
## symmetry_se 0.26648709 0.13410898 0.413506125 0.3947128
## fractal_dimension_se 0.24414277 0.12707090 0.427374207 0.8032688
## radius_worst 0.69720059 0.75737319 -0.230690710 0.2046072
## texture_worst 0.20037085 0.19649665 -0.074742965 0.1430026
## perimeter_worst 0.72103131 0.76121264 -0.217303755 0.2605158
## area_worst 0.73071297 0.81140796 -0.182195478 0.1993713
## smoothness_worst 0.13005439 0.12538943 0.314457456 0.2273942
## compactness_worst 0.34191945 0.28325654 -0.055558139 0.6787804
## concavity_worst 0.41889882 0.38510014 -0.058298387 0.6391467
## concave.points_worst 0.55489723 0.53816631 -0.102006796 0.4832083
## symmetry_worst 0.10993043 0.07412629 -0.107342098 0.2778784
## fractal_dimension_worst 0.08543257 0.01753930 0.101480315 0.5909728
## concavity_se concave.points_se symmetry_se
## radius_mean 0.1942036 0.37616896 -0.104320881
## texture_mean 0.1432931 0.16385103 0.009127168
## perimeter_mean 0.2280823 0.40721692 -0.081629327
## area_mean 0.2076601 0.37232028 -0.072496588
## smoothness_mean 0.2483957 0.38067569 0.200774376
## compactness_mean 0.5705169 0.64226185 0.229976591
## concavity_mean 0.6912702 0.68325992 0.178009208
## concave.points_mean 0.4391671 0.61563413 0.095350787
## symmetry_mean 0.3426270 0.39329787 0.449136542
## fractal_dimension_mean 0.4466303 0.34119804 0.345007397
## radius_se 0.3323575 0.51334644 0.240567362
## texture_se 0.1949985 0.23028340 0.411620680
## perimeter_se 0.3624816 0.55626408 0.266487092
## area_se 0.2708947 0.41572957 0.134108980
## smoothness_se 0.2686848 0.32842950 0.413506125
## compactness_se 0.8012683 0.74408267 0.394712835
## concavity_se 1.0000000 0.77180399 0.309428578
## concave.points_se 0.7718040 1.00000000 0.312780223
## symmetry_se 0.3094286 0.31278022 1.000000000
## fractal_dimension_se 0.7273722 0.61104414 0.369078083
## radius_worst 0.1869035 0.35812667 -0.128120769
## texture_worst 0.1002410 0.08674121 -0.077473420
## perimeter_worst 0.2266804 0.39499925 -0.103753044
## area_worst 0.1883527 0.34227116 -0.110342743
## smoothness_worst 0.1684813 0.21535060 -0.012661800
## compactness_worst 0.4848578 0.45288838 0.060254879
## concavity_worst 0.6625641 0.54959238 0.037119049
## concave.points_worst 0.4404723 0.60244961 -0.030413396
## symmetry_worst 0.1977878 0.14311567 0.389402485
## fractal_dimension_worst 0.4393293 0.31065455 0.078079476
## fractal_dimension_se radius_worst texture_worst
## radius_mean -0.042641269 0.96953897 0.297007644
## texture_mean 0.054457520 0.35257295 0.912044589
## perimeter_mean -0.005523391 0.96947636 0.303038372
## area_mean -0.019886963 0.96274609 0.287488627
## smoothness_mean 0.283606699 0.21312014 0.036071799
## compactness_mean 0.507318127 0.53531540 0.248132833
## concavity_mean 0.449300749 0.68823641 0.299878889
## concave.points_mean 0.257583746 0.83031763 0.292751713
## symmetry_mean 0.331786146 0.18572775 0.090650688
## fractal_dimension_mean 0.688131577 -0.25369149 -0.051269202
## radius_se 0.227753533 0.71506520 0.194798557
## texture_se 0.279722748 -0.11169031 0.409002766
## perimeter_se 0.244142773 0.69720059 0.200370854
## area_se 0.127070903 0.75737319 0.196496649
## smoothness_se 0.427374207 -0.23069071 -0.074742965
## compactness_se 0.803268818 0.20460717 0.143002583
## concavity_se 0.727372184 0.18690352 0.100240984
## concave.points_se 0.611044139 0.35812667 0.086741210
## symmetry_se 0.369078083 -0.12812077 -0.077473420
## fractal_dimension_se 1.000000000 -0.03748762 -0.003195029
## radius_worst -0.037487618 1.00000000 0.359920754
## texture_worst -0.003195029 0.35992075 1.000000000
## perimeter_worst -0.001000398 0.99370792 0.365098245
## area_worst -0.022736147 0.98401456 0.345842283
## smoothness_worst 0.170568316 0.21657443 0.225429415
## compactness_worst 0.390158842 0.47582004 0.360832339
## concavity_worst 0.379974661 0.57397471 0.368365607
## concave.points_worst 0.215204013 0.78742385 0.359754610
## symmetry_worst 0.111093956 0.24352920 0.233027461
## fractal_dimension_worst 0.591328066 0.09349198 0.219122425
## perimeter_worst area_worst smoothness_worst
## radius_mean 0.965136514 0.94108246 0.11961614
## texture_mean 0.358039575 0.34354595 0.07750336
## perimeter_mean 0.970386887 0.94154981 0.15054940
## area_mean 0.959119574 0.95921333 0.12352294
## smoothness_mean 0.238852626 0.20671836 0.80532420
## compactness_mean 0.590210428 0.50960381 0.56554117
## concavity_mean 0.729564917 0.67598723 0.44882204
## concave.points_mean 0.855923128 0.80962962 0.45275305
## symmetry_mean 0.219168559 0.17719338 0.42667503
## fractal_dimension_mean -0.205151211 -0.23185445 0.50494208
## radius_se 0.719683804 0.75154848 0.14191855
## texture_se -0.102241922 -0.08319499 -0.07365766
## perimeter_se 0.721031310 0.73071297 0.13005439
## area_se 0.761212636 0.81140796 0.12538943
## smoothness_se -0.217303755 -0.18219548 0.31445746
## compactness_se 0.260515840 0.19937133 0.22739423
## concavity_se 0.226680426 0.18835265 0.16848132
## concave.points_se 0.394999252 0.34227116 0.21535060
## symmetry_se -0.103753044 -0.11034274 -0.01266180
## fractal_dimension_se -0.001000398 -0.02273615 0.17056832
## radius_worst 0.993707916 0.98401456 0.21657443
## texture_worst 0.365098245 0.34584228 0.22542941
## perimeter_worst 1.000000000 0.97757809 0.23677460
## area_worst 0.977578091 1.00000000 0.20914533
## smoothness_worst 0.236774604 0.20914533 1.00000000
## compactness_worst 0.529407690 0.43829628 0.56818652
## concavity_worst 0.618344080 0.54333053 0.51852329
## concave.points_worst 0.816322102 0.74741880 0.54769090
## symmetry_worst 0.269492769 0.20914551 0.49383833
## fractal_dimension_worst 0.138956862 0.07964703 0.61762419
## compactness_worst concavity_worst concave.points_worst
## radius_mean 0.41346282 0.52691146 0.7442142
## texture_mean 0.27782959 0.30102522 0.2953158
## perimeter_mean 0.45577423 0.56387926 0.7712408
## area_mean 0.39041031 0.51260592 0.7220166
## smoothness_mean 0.47246844 0.43492571 0.5030534
## compactness_mean 0.86580904 0.81627525 0.8155732
## concavity_mean 0.75496802 0.88410264 0.8613230
## concave.points_mean 0.66745368 0.75239950 0.9101553
## symmetry_mean 0.47320001 0.43372101 0.4302966
## fractal_dimension_mean 0.45879816 0.34623388 0.1753254
## radius_se 0.28710317 0.38058463 0.5310623
## texture_se -0.09243935 -0.06895622 -0.1196375
## perimeter_se 0.34191945 0.41889882 0.5548972
## area_se 0.28325654 0.38510014 0.5381663
## smoothness_se -0.05555814 -0.05829839 -0.1020068
## compactness_se 0.67878035 0.63914670 0.4832083
## concavity_se 0.48485780 0.66256413 0.4404723
## concave.points_se 0.45288838 0.54959238 0.6024496
## symmetry_se 0.06025488 0.03711905 -0.0304134
## fractal_dimension_se 0.39015884 0.37997466 0.2152040
## radius_worst 0.47582004 0.57397471 0.7874239
## texture_worst 0.36083234 0.36836561 0.3597546
## perimeter_worst 0.52940769 0.61834408 0.8163221
## area_worst 0.43829628 0.54333053 0.7474188
## smoothness_worst 0.56818652 0.51852329 0.5476909
## compactness_worst 1.00000000 0.89226090 0.8010804
## concavity_worst 0.89226090 1.00000000 0.8554339
## concave.points_worst 0.80108036 0.85543386 1.0000000
## symmetry_worst 0.61444050 0.53251973 0.5025285
## fractal_dimension_worst 0.81045486 0.68651092 0.5111141
## symmetry_worst fractal_dimension_worst
## radius_mean 0.16395333 0.007065886
## texture_mean 0.10500791 0.119205351
## perimeter_mean 0.18911504 0.051018530
## area_mean 0.14356991 0.003737597
## smoothness_mean 0.39430948 0.499316369
## compactness_mean 0.51022343 0.687382323
## concavity_mean 0.40946413 0.514929891
## concave.points_mean 0.37574415 0.368661134
## symmetry_mean 0.69982580 0.438413498
## fractal_dimension_mean 0.33401868 0.767296779
## radius_se 0.09454283 0.049559432
## texture_se -0.12821476 -0.045654569
## perimeter_se 0.10993043 0.085432572
## area_se 0.07412629 0.017539295
## smoothness_se -0.10734210 0.101480315
## compactness_se 0.27787843 0.590972763
## concavity_se 0.19778782 0.439329269
## concave.points_se 0.14311567 0.310654551
## symmetry_se 0.38940248 0.078079476
## fractal_dimension_se 0.11109396 0.591328066
## radius_worst 0.24352920 0.093491979
## texture_worst 0.23302746 0.219122425
## perimeter_worst 0.26949277 0.138956862
## area_worst 0.20914551 0.079647034
## smoothness_worst 0.49383833 0.617624192
## compactness_worst 0.61444050 0.810454856
## concavity_worst 0.53251973 0.686510921
## concave.points_worst 0.50252849 0.511114146
## symmetry_worst 1.00000000 0.537848206
## fractal_dimension_worst 0.53784821 1.000000000
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = 0.95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
number.cex = 0.65,
p.mat = DPA_CorrelationTest$p,
insig = "blank")##################################
# Identifying the highly correlated variables
##################################
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.95))## [1] 15
if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 15 pairs of numeric variable(s) with Correlation.Coefficient>0.95."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 7 numeric variable(s)."
## [1] "Variable 1 for removal: perimeter_worst"
## [1] "Variable 2 for removal: radius_worst"
## [1] "Variable 3 for removal: perimeter_mean"
## [1] "Variable 4 for removal: area_worst"
## [1] "Variable 5 for removal: radius_mean"
## [1] "Variable 6 for removal: perimeter_se"
## [1] "Variable 7 for removal: area_se"
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0
if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."
##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Outliers <- boxplot.stats(DPA_BoxCoxTransformed[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA_BoxCoxTransformed[,i] %in% c(Outliers))
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}DPA_BoxCoxTransformed$diagnosis <- DPA[,c("diagnosis")]##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("concavity_se",
"perimeter_worst",
"radius_worst",
"perimeter_mean",
"area_worst",
"radius_mean",
"perimeter_se",
"area_se",
"concavity_mean",
"concave.points_mean",
"concave.points_se",
"concavity_worst")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 1138 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| diagnosis | 0 | 1 | FALSE | 2 | B: 714, M: 424 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| texture_mean | 0 | 1 | 2.94 | 0.22 | 2.27 | 2.78 | 2.94 | 3.08 | 3.67 | ▁▅▇▃▁ |
| area_mean | 0 | 1 | 6.36 | 0.48 | 4.97 | 6.04 | 6.31 | 6.66 | 7.82 | ▁▅▇▃▁ |
| smoothness_mean | 0 | 1 | -2.35 | 0.15 | -2.94 | -2.45 | -2.34 | -2.25 | -1.81 | ▁▂▇▃▁ |
| compactness_mean | 0 | 1 | -2.38 | 0.49 | -3.94 | -2.73 | -2.38 | -2.04 | -1.06 | ▁▅▇▇▂ |
| symmetry_mean | 0 | 1 | -2.26 | 0.25 | -3.20 | -2.42 | -2.25 | -2.10 | -1.43 | ▁▂▇▅▁ |
| fractal_dimension_mean | 0 | 1 | -130.58 | 26.03 | -199.82 | -149.68 | -131.52 | -113.87 | -52.16 | ▁▆▇▃▁ |
| radius_se | 0 | 1 | -1.42 | 0.81 | -3.51 | -1.98 | -1.42 | -0.86 | 0.86 | ▁▆▇▅▁ |
| texture_se | 0 | 1 | 0.10 | 0.43 | -1.02 | -0.18 | 0.10 | 0.39 | 1.59 | ▂▆▇▂▁ |
| smoothness_se | 0 | 1 | -11.83 | 1.66 | -19.20 | -12.84 | -11.85 | -10.78 | -6.11 | ▁▂▇▅▁ |
| compactness_se | 0 | 1 | -3.88 | 0.65 | -6.10 | -4.34 | -3.89 | -3.43 | -2.00 | ▁▃▇▆▁ |
| symmetry_se | 0 | 1 | -16.51 | 3.52 | -28.80 | -18.91 | -16.46 | -14.16 | -5.98 | ▁▃▇▅▁ |
| fractal_dimension_se | 0 | 1 | -15.48 | 2.88 | -24.04 | -17.43 | -15.37 | -13.46 | -6.23 | ▁▅▇▃▁ |
| texture_worst | 0 | 1 | 4.53 | 0.46 | 3.22 | 4.20 | 4.55 | 4.85 | 5.91 | ▁▅▇▅▁ |
| smoothness_worst | 0 | 1 | -1.52 | 0.09 | -1.82 | -1.58 | -1.52 | -1.46 | -1.21 | ▁▃▇▃▁ |
| compactness_worst | 0 | 1 | -1.55 | 0.62 | -3.60 | -1.92 | -1.55 | -1.08 | 0.06 | ▁▃▇▆▁ |
| concave.points_worst | 0 | 1 | 0.11 | 0.07 | 0.00 | 0.06 | 0.10 | 0.16 | 0.29 | ▅▇▅▃▁ |
| symmetry_worst | 0 | 1 | -1.77 | 0.37 | -3.06 | -2.00 | -1.76 | -1.55 | -0.45 | ▁▃▇▂▁ |
| fractal_dimension_worst | 0 | 1 | -19.62 | 4.79 | -32.59 | -22.99 | -19.73 | -16.32 | -5.17 | ▁▅▇▃▁ |
##################################
# Loading dataset
##################################
DPA <- PMA
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("diagnosis")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
ncol(DPA.Predictors.Numeric)## [1] 18
##################################
# Converting response variable data type to factor
##################################
DPA$diagnosis <- as.factor(DPA$diagnosis)
length(levels(DPA$diagnosis))## [1] 2
##################################
# Formulating the box plots
##################################
featurePlot(x = DPA.Predictors.Numeric,
y = DPA$diagnosis,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|",
layout = c(6, 3))##################################
# Obtaining the AUROC
##################################
AUROC <- filterVarImp(x = DPA.Predictors.Numeric,
y = DPA$diagnosis)
##################################
# Formulating the summary table
##################################
AUROC_Summary <- AUROC
AUROC_Summary$Predictor <- rownames(AUROC)
names(AUROC_Summary)[1] <- "AUROC"
AUROC_Summary$Metric <- rep("AUROC",nrow(AUROC))
AUROC_Summary[order(AUROC_Summary$AUROC, decreasing=TRUE),] ## AUROC B Predictor Metric
## concave.points_worst 0.9667037 0.9667037 concave.points_worst AUROC
## area_mean 0.9383159 0.9383159 area_mean AUROC
## radius_se 0.8683341 0.8683341 radius_se AUROC
## compactness_mean 0.8637823 0.8637823 compactness_mean AUROC
## compactness_worst 0.8623025 0.8623025 compactness_worst AUROC
## texture_worst 0.7846308 0.7846308 texture_worst AUROC
## texture_mean 0.7758245 0.7758245 texture_mean AUROC
## smoothness_worst 0.7540563 0.7540563 smoothness_worst AUROC
## symmetry_worst 0.7369391 0.7369391 symmetry_worst AUROC
## compactness_se 0.7272805 0.7272805 compactness_se AUROC
## smoothness_mean 0.7220416 0.7220416 smoothness_mean AUROC
## symmetry_mean 0.6985624 0.6985624 symmetry_mean AUROC
## fractal_dimension_worst 0.6859706 0.6859706 fractal_dimension_worst AUROC
## fractal_dimension_se 0.6203028 0.6203028 fractal_dimension_se AUROC
## symmetry_se 0.5551107 0.5551107 symmetry_se AUROC
## smoothness_se 0.5311625 0.5311625 smoothness_se AUROC
## fractal_dimension_mean 0.5154656 0.5154656 fractal_dimension_mean AUROC
## texture_se 0.5115943 0.5115943 texture_se AUROC
##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ AUROC | Metric,
AUROC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Creating the pre-modelling dataset
# into the train and test sets
##################################
DPA <- DPA[,colnames(DPA) %in% c("diagnosis",
"texture_worst",
"texture_mean",
"smoothness_worst",
"symmetry_worst",
"compactness_se",
"smoothness_mean")]
set.seed(12345678)
MA_Train_Index <- createDataPartition(DPA$diagnosis,p=0.8)[[1]]
MA_Train <- DPA[ MA_Train_Index, ]
MA_Test <- DPA[-MA_Train_Index, ]##################################
# Setting the cross validation process
# using the Repeated K-Fold
##################################
set.seed(12345678)
RKFold_Control <- trainControl(method="repeatedcv",
summaryFunction = twoClassSummary,
number=5,
repeats=5,
classProbs = TRUE)
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
AB_Grid = expand.grid(mfinal = c(50,100,100),
maxdepth = c(4,5,6),
coeflearn = "Breiman")
##################################
# Running the adaptive boosting model
# by setting the caret method to 'AdaBoost.M1'
##################################
set.seed(12345678)
MBS_AB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "AdaBoost.M1",
tuneGrid = AB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_AB_Tune## AdaBoost.M1
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## maxdepth mfinal ROC Sens Spec
## 4 50 0.9506186 0.8800000 0.9335561
## 4 100 0.9575073 0.8994118 0.9384409
## 5 50 0.9575898 0.8941176 0.9412265
## 5 100 0.9631176 0.8982353 0.9412265
## 6 50 0.9619147 0.8988235 0.9415927
## 6 100 0.9647554 0.9011765 0.9398352
##
## Tuning parameter 'coeflearn' was held constant at a value of Breiman
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were mfinal = 100, maxdepth = 6
## and coeflearn = Breiman.
MBS_AB_Tune$finalModel## $formula
## .outcome ~ .
## <environment: 0x000000003ec71dd8>
##
## $trees
## $trees[[1]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 345 B (0.378289474 0.621710526)
## 2) smoothness_worst>=-1.500665 380 139 M (0.634210526 0.365789474)
## 4) texture_mean>=2.931727 222 34 M (0.846846847 0.153153153)
## 8) symmetry_worst>=-1.45531 64 0 M (1.000000000 0.000000000) *
## 9) symmetry_worst< -1.45531 158 34 M (0.784810127 0.215189873)
## 18) smoothness_worst< -1.437613 101 11 M (0.891089109 0.108910891)
## 36) smoothness_worst< -1.483884 32 0 M (1.000000000 0.000000000) *
## 37) smoothness_worst>=-1.483884 69 11 M (0.840579710 0.159420290)
## 74) smoothness_worst>=-1.482699 66 8 M (0.878787879 0.121212121) *
## 75) smoothness_worst< -1.482699 3 0 B (0.000000000 1.000000000) *
## 19) smoothness_worst>=-1.437613 57 23 M (0.596491228 0.403508772)
## 38) smoothness_mean>=-2.292155 42 10 M (0.761904762 0.238095238)
## 76) smoothness_mean< -2.093138 36 5 M (0.861111111 0.138888889) *
## 77) smoothness_mean>=-2.093138 6 1 B (0.166666667 0.833333333) *
## 39) smoothness_mean< -2.292155 15 2 B (0.133333333 0.866666667)
## 78) texture_mean>=3.075523 3 1 M (0.666666667 0.333333333) *
## 79) texture_mean< 3.075523 12 0 B (0.000000000 1.000000000) *
## 5) texture_mean< 2.931727 158 53 B (0.335443038 0.664556962)
## 10) compactness_se>=-3.891799 76 29 M (0.618421053 0.381578947)
## 20) symmetry_worst>=-1.668672 45 6 M (0.866666667 0.133333333)
## 40) smoothness_mean< -1.889548 43 4 M (0.906976744 0.093023256)
## 80) compactness_se< -2.646661 42 3 M (0.928571429 0.071428571) *
## 81) compactness_se>=-2.646661 1 0 B (0.000000000 1.000000000) *
## 41) smoothness_mean>=-1.889548 2 0 B (0.000000000 1.000000000) *
## 21) symmetry_worst< -1.668672 31 8 B (0.258064516 0.741935484)
## 42) compactness_se< -3.854964 5 0 M (1.000000000 0.000000000) *
## 43) compactness_se>=-3.854964 26 3 B (0.115384615 0.884615385)
## 86) smoothness_worst< -1.493233 3 1 M (0.666666667 0.333333333) *
## 87) smoothness_worst>=-1.493233 23 1 B (0.043478261 0.956521739) *
## 11) compactness_se< -3.891799 82 6 B (0.073170732 0.926829268)
## 22) smoothness_worst< -1.49885 3 0 M (1.000000000 0.000000000) *
## 23) smoothness_worst>=-1.49885 79 3 B (0.037974684 0.962025316)
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## 47) texture_worst< 4.68139 76 1 B (0.013157895 0.986842105)
## 94) compactness_se>=-3.970723 12 1 B (0.083333333 0.916666667) *
## 95) compactness_se< -3.970723 64 0 B (0.000000000 1.000000000) *
## 3) smoothness_worst< -1.500665 532 104 B (0.195488722 0.804511278)
## 6) texture_mean>=3.007414 171 72 B (0.421052632 0.578947368)
## 12) compactness_se>=-3.021724 22 2 M (0.909090909 0.090909091)
## 24) texture_mean>=3.038537 20 0 M (1.000000000 0.000000000) *
## 25) texture_mean< 3.038537 2 0 B (0.000000000 1.000000000) *
## 13) compactness_se< -3.021724 149 52 B (0.348993289 0.651006711)
## 26) smoothness_mean>=-2.508076 113 52 B (0.460176991 0.539823009)
## 52) symmetry_worst>=-1.527595 16 2 M (0.875000000 0.125000000)
## 104) smoothness_worst< -1.513943 14 0 M (1.000000000 0.000000000) *
## 105) smoothness_worst>=-1.513943 2 0 B (0.000000000 1.000000000) *
## 53) symmetry_worst< -1.527595 97 38 B (0.391752577 0.608247423)
## 106) smoothness_mean< -2.503847 6 0 M (1.000000000 0.000000000) *
## 107) smoothness_mean>=-2.503847 91 32 B (0.351648352 0.648351648) *
## 27) smoothness_mean< -2.508076 36 0 B (0.000000000 1.000000000) *
## 7) texture_mean< 3.007414 361 32 B (0.088642659 0.911357341)
## 14) texture_worst>=4.888103 3 0 M (1.000000000 0.000000000) *
## 15) texture_worst< 4.888103 358 29 B (0.081005587 0.918994413)
## 30) compactness_se>=-3.953942 104 18 B (0.173076923 0.826923077)
## 60) compactness_se< -3.48221 39 17 B (0.435897436 0.564102564)
## 120) compactness_se>=-3.623844 16 3 M (0.812500000 0.187500000) *
## 121) compactness_se< -3.623844 23 4 B (0.173913043 0.826086957) *
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## 122) texture_mean>=2.984668 9 1 B (0.111111111 0.888888889) *
## 123) texture_mean< 2.984668 56 0 B (0.000000000 1.000000000) *
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## 127) texture_worst< 4.389974 176 1 B (0.005681818 0.994318182) *
##
## $trees[[2]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 382 B (0.41885965 0.58114035)
## 2) texture_mean>=2.927988 495 174 M (0.64848485 0.35151515)
## 4) smoothness_mean>=-2.425205 352 76 M (0.78409091 0.21590909)
## 8) compactness_se>=-3.797621 230 26 M (0.88695652 0.11304348)
## 16) symmetry_worst>=-2.184494 209 12 M (0.94258373 0.05741627)
## 32) texture_worst>=4.35267 207 10 M (0.95169082 0.04830918)
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## 33) texture_worst< 4.35267 2 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.184494 21 7 B (0.33333333 0.66666667)
## 34) symmetry_worst< -2.271177 7 0 M (1.00000000 0.00000000) *
## 35) symmetry_worst>=-2.271177 14 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.797621 122 50 M (0.59016393 0.40983607)
## 18) texture_mean>=3.057767 57 9 M (0.84210526 0.15789474)
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## 19) texture_mean< 3.057767 65 24 B (0.36923077 0.63076923)
## 38) smoothness_mean< -2.309577 26 8 M (0.69230769 0.30769231)
## 76) smoothness_worst>=-1.514694 15 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst< -1.514694 11 3 B (0.27272727 0.72727273) *
## 39) smoothness_mean>=-2.309577 39 6 B (0.15384615 0.84615385)
## 78) smoothness_mean>=-2.244788 12 5 B (0.41666667 0.58333333) *
## 79) smoothness_mean< -2.244788 27 1 B (0.03703704 0.96296296) *
## 5) smoothness_mean< -2.425205 143 45 B (0.31468531 0.68531469)
## 10) symmetry_worst>=-1.695215 47 22 M (0.53191489 0.46808511)
## 20) compactness_se< -4.088469 28 6 M (0.78571429 0.21428571)
## 40) texture_mean>=2.964399 22 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.964399 6 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.088469 19 3 B (0.15789474 0.84210526)
## 42) texture_worst>=5.003123 3 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.003123 16 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.695215 96 20 B (0.20833333 0.79166667)
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## 88) compactness_se< -2.942351 16 1 M (0.93750000 0.06250000) *
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## 45) texture_mean< 3.061509 5 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.556752 72 5 B (0.06944444 0.93055556)
## 46) compactness_se>=-3.612359 20 4 B (0.20000000 0.80000000)
## 92) compactness_se< -3.580055 2 0 M (1.00000000 0.00000000) *
## 93) compactness_se>=-3.580055 18 2 B (0.11111111 0.88888889) *
## 47) compactness_se< -3.612359 52 1 B (0.01923077 0.98076923)
## 94) texture_mean< 2.966301 11 1 B (0.09090909 0.90909091) *
## 95) texture_mean>=2.966301 41 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.927988 417 61 B (0.14628297 0.85371703)
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## 7) symmetry_worst< -1.36527 388 42 B (0.10824742 0.89175258)
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## 28) smoothness_mean< -2.467991 3 0 M (1.00000000 0.00000000) *
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## 58) texture_mean>=2.656737 211 38 B (0.18009479 0.81990521)
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## 118) smoothness_mean>=-2.074653 3 1 B (0.33333333 0.66666667) *
## 119) smoothness_mean< -2.074653 71 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.468758 100 0 B (0.00000000 1.00000000) *
##
## $trees[[3]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 415 B (0.45504386 0.54495614)
## 2) texture_worst>=4.275472 677 300 M (0.55686854 0.44313146)
## 4) smoothness_worst>=-1.556752 487 165 M (0.66119097 0.33880903)
## 8) symmetry_worst>=-2.027922 424 123 M (0.70990566 0.29009434)
## 16) smoothness_mean>=-2.469882 410 110 M (0.73170732 0.26829268)
## 32) symmetry_worst>=-1.329407 41 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -1.329407 369 110 M (0.70189702 0.29810298)
## 66) smoothness_worst< -1.51308 116 19 M (0.83620690 0.16379310) *
## 67) smoothness_worst>=-1.51308 253 91 M (0.64031621 0.35968379) *
## 17) smoothness_mean< -2.469882 14 1 B (0.07142857 0.92857143)
## 34) compactness_se>=-3.935569 1 0 M (1.00000000 0.00000000) *
## 35) compactness_se< -3.935569 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.027922 63 21 B (0.33333333 0.66666667)
## 18) texture_worst>=4.583884 42 21 M (0.50000000 0.50000000)
## 36) texture_worst< 5.117452 29 8 M (0.72413793 0.27586207)
## 72) compactness_se>=-4.170636 24 4 M (0.83333333 0.16666667) *
## 73) compactness_se< -4.170636 5 1 B (0.20000000 0.80000000) *
## 37) texture_worst>=5.117452 13 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 4.583884 21 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.556752 190 55 B (0.28947368 0.71052632)
## 10) symmetry_worst< -2.227786 23 7 M (0.69565217 0.30434783)
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## 11) symmetry_worst>=-2.227786 167 39 B (0.23353293 0.76646707)
## 22) symmetry_worst>=-1.238986 6 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst< -1.238986 161 33 B (0.20496894 0.79503106)
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## 94) smoothness_worst< -1.720903 7 2 M (0.71428571 0.28571429) *
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## 3) texture_worst< 4.275472 235 38 B (0.16170213 0.83829787)
## 6) compactness_se>=-3.958868 105 38 B (0.36190476 0.63809524)
## 12) symmetry_worst>=-1.42974 28 10 M (0.64285714 0.35714286)
## 24) compactness_se>=-3.391558 13 0 M (1.00000000 0.00000000) *
## 25) compactness_se< -3.391558 15 5 B (0.33333333 0.66666667)
## 50) smoothness_mean< -2.393992 5 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.393992 10 0 B (0.00000000 1.00000000) *
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## 26) compactness_se< -3.492332 43 19 B (0.44186047 0.55813953)
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## 27) compactness_se>=-3.492332 34 1 B (0.02941176 0.97058824)
## 54) compactness_se< -3.48221 6 1 B (0.16666667 0.83333333)
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## 109) texture_mean>=2.787307 5 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-3.48221 28 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.958868 130 0 B (0.00000000 1.00000000) *
##
## $trees[[4]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 364 B (0.39912281 0.60087719)
## 2) compactness_se>=-3.721197 380 152 M (0.60000000 0.40000000)
## 4) texture_mean>=3.054236 164 40 M (0.75609756 0.24390244)
## 8) symmetry_worst>=-2.029591 130 20 M (0.84615385 0.15384615)
## 16) smoothness_mean>=-2.41714 98 6 M (0.93877551 0.06122449)
## 32) smoothness_mean< -2.105484 92 3 M (0.96739130 0.03260870)
## 64) smoothness_worst>=-1.609426 91 2 M (0.97802198 0.02197802) *
## 65) smoothness_worst< -1.609426 1 0 B (0.00000000 1.00000000) *
## 33) smoothness_mean>=-2.105484 6 3 M (0.50000000 0.50000000)
## 66) texture_mean< 3.186512 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.186512 3 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.41714 32 14 M (0.56250000 0.43750000)
## 34) smoothness_mean< -2.453967 19 1 M (0.94736842 0.05263158)
## 68) smoothness_worst>=-1.612487 18 0 M (1.00000000 0.00000000) *
## 69) smoothness_worst< -1.612487 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.453967 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.029591 34 14 B (0.41176471 0.58823529)
## 18) texture_worst< 4.645452 10 0 M (1.00000000 0.00000000) *
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## 77) texture_mean< 3.208081 2 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst>=-2.242858 18 0 B (0.00000000 1.00000000) *
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## 10) compactness_se< -3.427747 107 31 M (0.71028037 0.28971963)
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## 80) smoothness_mean>=-2.443631 81 8 M (0.90123457 0.09876543) *
## 81) smoothness_mean< -2.443631 11 3 B (0.27272727 0.72727273) *
## 41) texture_mean>=3.040702 7 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.628033 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-3.427747 109 28 B (0.25688073 0.74311927)
## 22) symmetry_worst>=-1.300369 16 1 M (0.93750000 0.06250000)
## 44) compactness_se< -2.524297 15 0 M (1.00000000 0.00000000) *
## 45) compactness_se>=-2.524297 1 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.300369 93 13 B (0.13978495 0.86021505)
## 46) symmetry_worst>=-1.853888 48 13 B (0.27083333 0.72916667)
## 92) texture_mean>=2.96681 19 9 M (0.52631579 0.47368421) *
## 93) texture_mean< 2.96681 29 3 B (0.10344828 0.89655172) *
## 47) symmetry_worst< -1.853888 45 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 532 136 B (0.25563910 0.74436090)
## 6) texture_worst>=4.36289 386 128 B (0.33160622 0.66839378)
## 12) smoothness_worst>=-1.424105 30 5 M (0.83333333 0.16666667)
## 24) smoothness_mean>=-2.397334 25 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.397334 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.424105 356 103 B (0.28932584 0.71067416)
## 26) smoothness_mean< -2.260964 281 99 B (0.35231317 0.64768683)
## 52) smoothness_mean>=-2.272056 8 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.272056 273 91 B (0.33333333 0.66666667)
## 106) compactness_se< -4.039628 175 74 B (0.42285714 0.57714286) *
## 107) compactness_se>=-4.039628 98 17 B (0.17346939 0.82653061) *
## 27) smoothness_mean>=-2.260964 75 4 B (0.05333333 0.94666667)
## 54) texture_mean< 2.844609 2 1 M (0.50000000 0.50000000)
## 108) texture_mean>=2.831705 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.831705 1 0 B (0.00000000 1.00000000) *
## 55) texture_mean>=2.844609 73 3 B (0.04109589 0.95890411)
## 110) symmetry_worst>=-1.611386 23 3 B (0.13043478 0.86956522) *
## 111) symmetry_worst< -1.611386 50 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.36289 146 8 B (0.05479452 0.94520548)
## 14) symmetry_worst>=-1.428729 6 2 M (0.66666667 0.33333333)
## 28) texture_mean>=2.772337 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.772337 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.428729 140 4 B (0.02857143 0.97142857)
## 30) smoothness_mean>=-2.081877 2 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.081877 138 2 B (0.01449275 0.98550725)
## 62) compactness_se>=-3.892047 15 2 B (0.13333333 0.86666667)
## 124) compactness_se< -3.878107 2 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.878107 13 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.892047 123 0 B (0.00000000 1.00000000) *
##
## $trees[[5]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 444 B (0.48684211 0.51315789)
## 2) texture_mean>=2.811204 751 326 M (0.56591212 0.43408788)
## 4) compactness_se>=-4.779408 733 308 M (0.57980900 0.42019100)
## 8) texture_mean>=3.116842 176 46 M (0.73863636 0.26136364)
## 16) smoothness_mean>=-2.489159 154 30 M (0.80519481 0.19480519)
## 32) compactness_se>=-4.543049 146 22 M (0.84931507 0.15068493)
## 64) smoothness_mean< -2.099273 141 17 M (0.87943262 0.12056738) *
## 65) smoothness_mean>=-2.099273 5 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.543049 8 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.489159 22 6 B (0.27272727 0.72727273)
## 34) texture_mean< 3.190706 6 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=3.190706 16 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 3.116842 557 262 M (0.52962298 0.47037702)
## 18) symmetry_worst>=-1.107986 24 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst< -1.107986 533 262 M (0.50844278 0.49155722)
## 38) symmetry_worst>=-1.862978 386 170 M (0.55958549 0.44041451)
## 76) texture_mean< 3.11507 374 158 M (0.57754011 0.42245989) *
## 77) texture_mean>=3.11507 12 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst< -1.862978 147 55 B (0.37414966 0.62585034)
## 78) texture_worst>=4.905691 8 0 M (1.00000000 0.00000000) *
## 79) texture_worst< 4.905691 139 47 B (0.33812950 0.66187050) *
## 5) compactness_se< -4.779408 18 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.811204 161 19 B (0.11801242 0.88198758)
## 6) symmetry_worst>=-1.281003 5 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.281003 156 14 B (0.08974359 0.91025641)
## 14) smoothness_mean>=-1.977294 4 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -1.977294 152 10 B (0.06578947 0.93421053)
## 30) smoothness_mean>=-2.321264 59 9 B (0.15254237 0.84745763)
## 60) smoothness_mean< -2.287239 22 9 B (0.40909091 0.59090909)
## 120) texture_worst>=4.138116 5 0 M (1.00000000 0.00000000) *
## 121) texture_worst< 4.138116 17 4 B (0.23529412 0.76470588) *
## 61) smoothness_mean>=-2.287239 37 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.321264 93 1 B (0.01075269 0.98924731)
## 62) compactness_se>=-3.488718 12 1 B (0.08333333 0.91666667)
## 124) compactness_se< -3.483667 1 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.483667 11 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.488718 81 0 B (0.00000000 1.00000000) *
##
## $trees[[6]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 393 B (0.43092105 0.56907895)
## 2) texture_mean>=3.058002 294 107 M (0.63605442 0.36394558)
## 4) symmetry_worst>=-1.71268 124 21 M (0.83064516 0.16935484)
## 8) texture_worst>=4.818867 96 7 M (0.92708333 0.07291667)
## 16) smoothness_mean>=-2.509617 95 6 M (0.93684211 0.06315789)
## 32) smoothness_mean>=-2.334545 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.334545 41 6 M (0.85365854 0.14634146)
## 66) smoothness_mean< -2.347634 38 3 M (0.92105263 0.07894737) *
## 67) smoothness_mean>=-2.347634 3 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.509617 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.818867 28 14 M (0.50000000 0.50000000)
## 18) texture_worst< 4.790105 13 0 M (1.00000000 0.00000000) *
## 19) texture_worst>=4.790105 15 1 B (0.06666667 0.93333333)
## 38) smoothness_mean< -2.321477 1 0 M (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.321477 14 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.71268 170 84 B (0.49411765 0.50588235)
## 10) symmetry_worst< -1.733593 145 61 M (0.57931034 0.42068966)
## 20) smoothness_worst>=-1.603555 118 38 M (0.67796610 0.32203390)
## 40) symmetry_worst>=-2.184494 91 20 M (0.78021978 0.21978022)
## 80) smoothness_worst< -1.415354 81 11 M (0.86419753 0.13580247) *
## 81) smoothness_worst>=-1.415354 10 1 B (0.10000000 0.90000000) *
## 41) symmetry_worst< -2.184494 27 9 B (0.33333333 0.66666667)
## 82) smoothness_mean< -2.437515 6 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.437515 21 3 B (0.14285714 0.85714286) *
## 21) smoothness_worst< -1.603555 27 4 B (0.14814815 0.85185185)
## 42) smoothness_mean>=-2.373736 3 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.373736 24 1 B (0.04166667 0.95833333)
## 86) texture_worst< 4.508695 2 1 M (0.50000000 0.50000000) *
## 87) texture_worst>=4.508695 22 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.733593 25 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.058002 618 206 B (0.33333333 0.66666667)
## 6) texture_mean>=2.709047 536 200 B (0.37313433 0.62686567)
## 12) smoothness_worst>=-1.374428 14 1 M (0.92857143 0.07142857)
## 24) symmetry_worst>=-1.846189 13 0 M (1.00000000 0.00000000) *
## 25) symmetry_worst< -1.846189 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.374428 522 187 B (0.35823755 0.64176245)
## 26) compactness_se>=-4.717333 489 186 B (0.38036810 0.61963190)
## 52) compactness_se< -4.594248 32 9 M (0.71875000 0.28125000)
## 104) smoothness_worst< -1.54201 27 4 M (0.85185185 0.14814815) *
## 105) smoothness_worst>=-1.54201 5 0 B (0.00000000 1.00000000) *
## 53) compactness_se>=-4.594248 457 163 B (0.35667396 0.64332604)
## 106) smoothness_mean>=-2.434347 375 150 B (0.40000000 0.60000000) *
## 107) smoothness_mean< -2.434347 82 13 B (0.15853659 0.84146341) *
## 27) compactness_se< -4.717333 33 1 B (0.03030303 0.96969697)
## 54) texture_mean>=2.991714 1 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 2.991714 32 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.709047 82 6 B (0.07317073 0.92682927)
## 14) symmetry_worst>=-1.122487 2 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst< -1.122487 80 4 B (0.05000000 0.95000000)
## 30) texture_mean< 2.487336 8 2 B (0.25000000 0.75000000)
## 60) texture_mean>=2.434062 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.434062 6 0 B (0.00000000 1.00000000) *
## 31) texture_mean>=2.487336 72 2 B (0.02777778 0.97222222)
## 62) symmetry_worst< -2.111279 14 2 B (0.14285714 0.85714286)
## 124) smoothness_worst>=-1.49704 2 0 M (1.00000000 0.00000000) *
## 125) smoothness_worst< -1.49704 12 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-2.111279 58 0 B (0.00000000 1.00000000) *
##
## $trees[[7]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 M (0.51206140 0.48793860)
## 2) texture_mean>=2.707375 842 379 M (0.54988124 0.45011876)
## 4) compactness_se>=-4.706178 803 340 M (0.57658780 0.42341220)
## 8) symmetry_worst>=-1.329407 50 7 M (0.86000000 0.14000000)
## 16) texture_mean< 3.099059 39 1 M (0.97435897 0.02564103)
## 32) texture_mean>=2.756192 38 0 M (1.00000000 0.00000000) *
## 33) texture_mean< 2.756192 1 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.099059 11 5 B (0.45454545 0.54545455)
## 34) texture_mean>=3.141437 5 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.141437 6 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.329407 753 333 M (0.55776892 0.44223108)
## 18) smoothness_mean>=-2.546123 734 315 M (0.57084469 0.42915531)
## 36) symmetry_worst< -1.925345 193 63 M (0.67357513 0.32642487)
## 72) smoothness_worst>=-1.604472 169 44 M (0.73964497 0.26035503) *
## 73) smoothness_worst< -1.604472 24 5 B (0.20833333 0.79166667) *
## 37) symmetry_worst>=-1.925345 541 252 M (0.53419593 0.46580407)
## 74) smoothness_worst< -1.596198 49 8 M (0.83673469 0.16326531) *
## 75) smoothness_worst>=-1.596198 492 244 M (0.50406504 0.49593496) *
## 19) smoothness_mean< -2.546123 19 1 B (0.05263158 0.94736842)
## 38) smoothness_worst< -1.720903 4 1 B (0.25000000 0.75000000)
## 76) compactness_se>=-3.013033 1 0 M (1.00000000 0.00000000) *
## 77) compactness_se< -3.013033 3 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.720903 15 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -4.706178 39 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.707375 70 4 B (0.05714286 0.94285714)
## 6) symmetry_worst>=-1.15097 2 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.15097 68 2 B (0.02941176 0.97058824)
## 14) smoothness_mean>=-2.074653 3 1 M (0.66666667 0.33333333)
## 28) texture_mean>=2.434062 2 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.434062 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.074653 65 0 B (0.00000000 1.00000000) *
##
## $trees[[8]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) texture_mean>=2.709047 846 423 M (0.50000000 0.50000000)
## 4) symmetry_worst>=-1.329407 52 6 M (0.88461538 0.11538462)
## 8) texture_mean>=2.742062 50 4 M (0.92000000 0.08000000)
## 16) texture_mean< 3.10949 38 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.10949 12 4 M (0.66666667 0.33333333)
## 34) texture_mean>=3.126045 8 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.126045 4 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.742062 2 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.329407 794 377 B (0.47481108 0.52518892)
## 10) compactness_se>=-4.705732 765 377 B (0.49281046 0.50718954)
## 20) compactness_se< -4.448167 113 30 M (0.73451327 0.26548673)
## 40) smoothness_mean< -2.295268 101 18 M (0.82178218 0.17821782)
## 80) symmetry_worst< -1.478833 95 12 M (0.87368421 0.12631579) *
## 81) symmetry_worst>=-1.478833 6 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.295268 12 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.448167 652 294 B (0.45092025 0.54907975)
## 42) texture_worst< 4.642157 366 177 M (0.51639344 0.48360656)
## 84) smoothness_worst>=-1.456304 54 7 M (0.87037037 0.12962963) *
## 85) smoothness_worst< -1.456304 312 142 B (0.45512821 0.54487179) *
## 43) texture_worst>=4.642157 286 105 B (0.36713287 0.63286713)
## 86) texture_worst>=5.03133 54 21 M (0.61111111 0.38888889) *
## 87) texture_worst< 5.03133 232 72 B (0.31034483 0.68965517) *
## 11) compactness_se< -4.705732 29 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.709047 66 6 B (0.09090909 0.90909091)
## 6) symmetry_worst>=-1.122487 3 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.122487 63 3 B (0.04761905 0.95238095)
## 14) smoothness_mean>=-2.074653 10 2 B (0.20000000 0.80000000)
## 28) smoothness_mean< -2.060513 2 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.060513 8 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.074653 53 1 B (0.01886792 0.98113208)
## 30) symmetry_worst< -2.105665 6 1 B (0.16666667 0.83333333)
## 60) smoothness_mean>=-2.312057 1 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.312057 5 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.105665 47 0 B (0.00000000 1.00000000) *
##
## $trees[[9]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 B (0.48464912 0.51535088)
## 2) texture_mean>=2.824054 787 373 M (0.52604828 0.47395172)
## 4) texture_worst< 4.982753 644 274 M (0.57453416 0.42546584)
## 8) compactness_se>=-4.705732 622 254 M (0.59163987 0.40836013)
## 16) smoothness_mean< -2.352368 300 93 M (0.69000000 0.31000000)
## 32) compactness_se< -4.099264 144 24 M (0.83333333 0.16666667)
## 64) texture_mean>=2.947329 93 6 M (0.93548387 0.06451613) *
## 65) texture_mean< 2.947329 51 18 M (0.64705882 0.35294118) *
## 33) compactness_se>=-4.099264 156 69 M (0.55769231 0.44230769)
## 66) smoothness_mean>=-2.394871 63 10 M (0.84126984 0.15873016) *
## 67) smoothness_mean< -2.394871 93 34 B (0.36559140 0.63440860) *
## 17) smoothness_mean>=-2.352368 322 161 M (0.50000000 0.50000000)
## 34) symmetry_worst>=-1.529476 58 12 M (0.79310345 0.20689655)
## 68) compactness_se>=-4.127915 46 4 M (0.91304348 0.08695652) *
## 69) compactness_se< -4.127915 12 4 B (0.33333333 0.66666667) *
## 35) symmetry_worst< -1.529476 264 115 B (0.43560606 0.56439394)
## 70) smoothness_mean>=-2.332015 245 115 B (0.46938776 0.53061224) *
## 71) smoothness_mean< -2.332015 19 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.705732 22 2 B (0.09090909 0.90909091)
## 18) symmetry_worst>=-1.179946 2 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst< -1.179946 20 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.982753 143 44 B (0.30769231 0.69230769)
## 10) compactness_se>=-3.334337 23 6 M (0.73913043 0.26086957)
## 20) texture_worst>=4.998431 17 0 M (1.00000000 0.00000000) *
## 21) texture_worst< 4.998431 6 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.334337 120 27 B (0.22500000 0.77500000)
## 22) smoothness_mean>=-2.362094 47 19 B (0.40425532 0.59574468)
## 44) smoothness_worst< -1.450409 22 5 M (0.77272727 0.22727273)
## 88) symmetry_worst>=-2.207988 17 0 M (1.00000000 0.00000000) *
## 89) symmetry_worst< -2.207988 5 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.450409 25 2 B (0.08000000 0.92000000)
## 90) symmetry_worst>=-1.24413 1 0 M (1.00000000 0.00000000) *
## 91) symmetry_worst< -1.24413 24 1 B (0.04166667 0.95833333) *
## 23) smoothness_mean< -2.362094 73 8 B (0.10958904 0.89041096)
## 46) symmetry_worst>=-1.554775 3 0 M (1.00000000 0.00000000) *
## 47) symmetry_worst< -1.554775 70 5 B (0.07142857 0.92857143)
## 94) texture_worst>=5.636459 1 0 M (1.00000000 0.00000000) *
## 95) texture_worst< 5.636459 69 4 B (0.05797101 0.94202899) *
## 3) texture_mean< 2.824054 125 28 B (0.22400000 0.77600000)
## 6) compactness_se>=-3.764682 55 26 B (0.47272727 0.52727273)
## 12) smoothness_mean>=-2.31958 36 13 M (0.63888889 0.36111111)
## 24) texture_worst>=3.973898 24 4 M (0.83333333 0.16666667)
## 48) compactness_se< -3.364454 14 0 M (1.00000000 0.00000000) *
## 49) compactness_se>=-3.364454 10 4 M (0.60000000 0.40000000)
## 98) symmetry_worst>=-1.316602 6 0 M (1.00000000 0.00000000) *
## 99) symmetry_worst< -1.316602 4 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.973898 12 3 B (0.25000000 0.75000000)
## 50) compactness_se< -3.688804 2 0 M (1.00000000 0.00000000) *
## 51) compactness_se>=-3.688804 10 1 B (0.10000000 0.90000000)
## 102) texture_mean< 2.366153 1 0 M (1.00000000 0.00000000) *
## 103) texture_mean>=2.366153 9 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.31958 19 3 B (0.15789474 0.84210526)
## 26) symmetry_worst< -1.982852 3 1 M (0.66666667 0.33333333)
## 52) texture_mean>=2.763153 2 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.763153 1 0 B (0.00000000 1.00000000) *
## 27) symmetry_worst>=-1.982852 16 1 B (0.06250000 0.93750000)
## 54) smoothness_worst>=-1.493125 2 1 M (0.50000000 0.50000000)
## 108) texture_mean< 2.774841 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.774841 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst< -1.493125 14 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.764682 70 2 B (0.02857143 0.97142857)
## 14) symmetry_worst>=-1.431268 3 1 B (0.33333333 0.66666667)
## 28) texture_mean>=2.799919 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.799919 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.431268 67 1 B (0.01492537 0.98507463)
## 30) compactness_se>=-3.894783 8 1 B (0.12500000 0.87500000)
## 60) compactness_se< -3.866661 1 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-3.866661 7 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.894783 59 0 B (0.00000000 1.00000000) *
##
## $trees[[10]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 M (0.51973684 0.48026316)
## 2) texture_worst>=4.260219 794 348 M (0.56171285 0.43828715)
## 4) compactness_se< -2.927016 740 305 M (0.58783784 0.41216216)
## 8) compactness_se>=-3.322182 70 9 M (0.87142857 0.12857143)
## 16) smoothness_worst>=-1.507356 35 0 M (1.00000000 0.00000000) *
## 17) smoothness_worst< -1.507356 35 9 M (0.74285714 0.25714286)
## 34) smoothness_worst< -1.51411 28 2 M (0.92857143 0.07142857)
## 68) texture_mean>=2.988153 26 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.988153 2 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.51411 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.322182 670 296 M (0.55820896 0.44179104)
## 18) texture_worst< 4.642157 389 145 M (0.62724936 0.37275064)
## 36) symmetry_worst< -1.559263 324 102 M (0.68518519 0.31481481)
## 72) compactness_se>=-4.614925 306 84 M (0.72549020 0.27450980) *
## 73) compactness_se< -4.614925 18 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst>=-1.559263 65 22 B (0.33846154 0.66153846)
## 74) texture_worst>=4.614159 15 1 M (0.93333333 0.06666667) *
## 75) texture_worst< 4.614159 50 8 B (0.16000000 0.84000000) *
## 19) texture_worst>=4.642157 281 130 B (0.46263345 0.53736655)
## 38) symmetry_worst>=-1.591238 69 17 M (0.75362319 0.24637681)
## 76) texture_mean< 3.095125 45 3 M (0.93333333 0.06666667) *
## 77) texture_mean>=3.095125 24 10 B (0.41666667 0.58333333) *
## 39) symmetry_worst< -1.591238 212 78 B (0.36792453 0.63207547)
## 78) texture_worst>=4.682677 181 78 B (0.43093923 0.56906077) *
## 79) texture_worst< 4.682677 31 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-2.927016 54 11 B (0.20370370 0.79629630)
## 10) smoothness_worst>=-1.397207 7 0 M (1.00000000 0.00000000) *
## 11) smoothness_worst< -1.397207 47 4 B (0.08510638 0.91489362)
## 22) symmetry_worst< -2.040594 3 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst>=-2.040594 44 1 B (0.02272727 0.97727273)
## 46) texture_mean>=3.063534 5 1 B (0.20000000 0.80000000)
## 92) texture_mean< 3.166628 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean>=3.166628 4 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 3.063534 39 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.260219 118 28 B (0.23728814 0.76271186)
## 6) compactness_se>=-3.97985 73 28 B (0.38356164 0.61643836)
## 12) texture_mean>=2.863053 9 1 M (0.88888889 0.11111111)
## 24) smoothness_mean>=-2.316299 8 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean< -2.316299 1 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.863053 64 20 B (0.31250000 0.68750000)
## 26) symmetry_worst>=-1.281003 5 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst< -1.281003 59 15 B (0.25423729 0.74576271)
## 54) compactness_se< -3.866661 7 1 M (0.85714286 0.14285714)
## 108) texture_worst>=4.110502 6 0 M (1.00000000 0.00000000) *
## 109) texture_worst< 4.110502 1 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-3.866661 52 9 B (0.17307692 0.82692308)
## 110) texture_mean< 2.525679 3 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.525679 49 6 B (0.12244898 0.87755102) *
## 7) compactness_se< -3.97985 45 0 B (0.00000000 1.00000000) *
##
## $trees[[11]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 419 M (0.54057018 0.45942982)
## 2) texture_worst>=4.517889 608 231 M (0.62006579 0.37993421)
## 4) texture_worst< 4.54138 80 6 M (0.92500000 0.07500000)
## 8) symmetry_worst< -1.433387 78 4 M (0.94871795 0.05128205)
## 16) compactness_se< -3.16075 75 2 M (0.97333333 0.02666667)
## 32) smoothness_mean>=-2.440377 74 1 M (0.98648649 0.01351351)
## 64) texture_mean< 3.086942 68 0 M (1.00000000 0.00000000) *
## 65) texture_mean>=3.086942 6 1 M (0.83333333 0.16666667) *
## 33) smoothness_mean< -2.440377 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.16075 3 1 B (0.33333333 0.66666667)
## 34) texture_mean>=3.023554 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.023554 2 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.433387 2 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.54138 528 225 M (0.57386364 0.42613636)
## 10) texture_worst>=4.569492 494 192 M (0.61133603 0.38866397)
## 20) compactness_se>=-4.505325 423 144 M (0.65957447 0.34042553)
## 40) smoothness_worst< -1.400053 388 119 M (0.69329897 0.30670103)
## 80) smoothness_worst>=-1.672049 381 112 M (0.70603675 0.29396325) *
## 81) smoothness_worst< -1.672049 7 0 B (0.00000000 1.00000000) *
## 41) smoothness_worst>=-1.400053 35 10 B (0.28571429 0.71428571)
## 82) texture_mean>=3.044522 9 1 M (0.88888889 0.11111111) *
## 83) texture_mean< 3.044522 26 2 B (0.07692308 0.92307692) *
## 21) compactness_se< -4.505325 71 23 B (0.32394366 0.67605634)
## 42) smoothness_worst< -1.549205 36 16 M (0.55555556 0.44444444)
## 84) symmetry_worst>=-1.909332 26 6 M (0.76923077 0.23076923) *
## 85) symmetry_worst< -1.909332 10 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst>=-1.549205 35 3 B (0.08571429 0.91428571)
## 86) symmetry_worst< -1.696111 12 3 B (0.25000000 0.75000000) *
## 87) symmetry_worst>=-1.696111 23 0 B (0.00000000 1.00000000) *
## 11) texture_worst< 4.569492 34 1 B (0.02941176 0.97058824)
## 22) texture_mean>=3.034949 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.034949 33 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.517889 304 116 B (0.38157895 0.61842105)
## 6) compactness_se>=-3.766631 127 59 M (0.53543307 0.46456693)
## 12) smoothness_mean>=-2.454939 106 40 M (0.62264151 0.37735849)
## 24) compactness_se< -3.427747 57 12 M (0.78947368 0.21052632)
## 48) symmetry_worst< -1.461208 52 7 M (0.86538462 0.13461538)
## 96) texture_worst< 4.460444 50 5 M (0.90000000 0.10000000) *
## 97) texture_worst>=4.460444 2 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst>=-1.461208 5 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.427747 49 21 B (0.42857143 0.57142857)
## 50) smoothness_mean>=-2.149436 10 1 M (0.90000000 0.10000000)
## 100) compactness_se>=-3.412571 9 0 M (1.00000000 0.00000000) *
## 101) compactness_se< -3.412571 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.149436 39 12 B (0.30769231 0.69230769)
## 102) texture_mean>=2.96681 9 1 M (0.88888889 0.11111111) *
## 103) texture_mean< 2.96681 30 4 B (0.13333333 0.86666667) *
## 13) smoothness_mean< -2.454939 21 2 B (0.09523810 0.90476190)
## 26) texture_mean>=3.038737 2 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 3.038737 19 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.766631 177 48 B (0.27118644 0.72881356)
## 14) texture_mean>=2.812409 114 47 B (0.41228070 0.58771930)
## 28) smoothness_worst>=-1.451541 14 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.451541 100 33 B (0.33000000 0.67000000)
## 58) smoothness_worst< -1.538735 64 31 M (0.51562500 0.48437500)
## 116) symmetry_worst< -1.548429 54 21 M (0.61111111 0.38888889) *
## 117) symmetry_worst>=-1.548429 10 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst>=-1.538735 36 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 2.812409 63 1 B (0.01587302 0.98412698)
## 30) symmetry_worst< -1.930267 13 1 B (0.07692308 0.92307692)
## 60) symmetry_worst>=-1.95343 1 0 M (1.00000000 0.00000000) *
## 61) symmetry_worst< -1.95343 12 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-1.930267 50 0 B (0.00000000 1.00000000) *
##
## $trees[[12]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) smoothness_mean>=-2.424301 659 326 M (0.50531108 0.49468892)
## 4) smoothness_mean< -2.382983 117 39 M (0.66666667 0.33333333)
## 8) texture_worst>=4.611234 68 5 M (0.92647059 0.07352941)
## 16) smoothness_worst>=-1.586424 64 2 M (0.96875000 0.03125000)
## 32) symmetry_worst>=-2.212871 62 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.586424 4 1 B (0.25000000 0.75000000)
## 34) smoothness_mean>=-2.4008 1 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.4008 3 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.611234 49 15 B (0.30612245 0.69387755)
## 18) smoothness_mean< -2.411844 16 4 M (0.75000000 0.25000000)
## 36) smoothness_worst< -1.538735 10 0 M (1.00000000 0.00000000) *
## 37) smoothness_worst>=-1.538735 6 2 B (0.33333333 0.66666667)
## 74) smoothness_mean< -2.421763 2 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean>=-2.421763 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.411844 33 3 B (0.09090909 0.90909091)
## 38) texture_mean>=2.964668 4 1 M (0.75000000 0.25000000)
## 76) smoothness_mean>=-2.394659 3 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.394659 1 0 B (0.00000000 1.00000000) *
## 39) texture_mean< 2.964668 29 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.382983 542 255 B (0.47047970 0.52952030)
## 10) compactness_se>=-4.025757 373 169 M (0.54691689 0.45308311)
## 20) smoothness_mean>=-2.333148 292 111 M (0.61986301 0.38013699)
## 40) symmetry_worst>=-1.839419 204 56 M (0.72549020 0.27450980)
## 80) texture_worst>=4.508732 140 21 M (0.85000000 0.15000000) *
## 81) texture_worst< 4.508732 64 29 B (0.45312500 0.54687500) *
## 41) symmetry_worst< -1.839419 88 33 B (0.37500000 0.62500000)
## 82) symmetry_worst< -1.925345 62 30 M (0.51612903 0.48387097) *
## 83) symmetry_worst>=-1.925345 26 1 B (0.03846154 0.96153846) *
## 21) smoothness_mean< -2.333148 81 23 B (0.28395062 0.71604938)
## 42) symmetry_worst< -1.571577 52 23 B (0.44230769 0.55769231)
## 84) symmetry_worst>=-1.716495 18 3 M (0.83333333 0.16666667) *
## 85) symmetry_worst< -1.716495 34 8 B (0.23529412 0.76470588) *
## 43) symmetry_worst>=-1.571577 29 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.025757 169 51 B (0.30177515 0.69822485)
## 22) smoothness_mean< -2.294121 82 36 M (0.56097561 0.43902439)
## 44) texture_worst>=4.376622 69 23 M (0.66666667 0.33333333)
## 88) texture_worst< 4.626933 29 0 M (1.00000000 0.00000000) *
## 89) texture_worst>=4.626933 40 17 B (0.42500000 0.57500000) *
## 45) texture_worst< 4.376622 13 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.294121 87 5 B (0.05747126 0.94252874)
## 46) smoothness_mean>=-2.21595 20 5 B (0.25000000 0.75000000)
## 92) smoothness_mean< -2.210016 6 1 M (0.83333333 0.16666667) *
## 93) smoothness_mean>=-2.210016 14 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean< -2.21595 67 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.424301 253 77 B (0.30434783 0.69565217)
## 6) texture_mean>=2.963209 154 65 B (0.42207792 0.57792208)
## 12) texture_mean< 3.176386 107 48 M (0.55140187 0.44859813)
## 24) texture_mean>=3.129791 26 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.129791 81 33 B (0.40740741 0.59259259)
## 50) symmetry_worst>=-1.54778 10 0 M (1.00000000 0.00000000) *
## 51) symmetry_worst< -1.54778 71 23 B (0.32394366 0.67605634)
## 102) smoothness_mean< -2.478376 44 21 M (0.52272727 0.47727273) *
## 103) smoothness_mean>=-2.478376 27 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=3.176386 47 6 B (0.12765957 0.87234043)
## 26) symmetry_worst>=-1.530091 3 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst< -1.530091 44 3 B (0.06818182 0.93181818)
## 54) symmetry_worst< -2.188379 3 1 M (0.66666667 0.33333333)
## 108) texture_mean>=3.330945 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 3.330945 1 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst>=-2.188379 41 1 B (0.02439024 0.97560976)
## 110) smoothness_worst>=-1.490267 3 1 B (0.33333333 0.66666667) *
## 111) smoothness_worst< -1.490267 38 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.963209 99 12 B (0.12121212 0.87878788)
## 14) smoothness_worst>=-1.554151 29 8 B (0.27586207 0.72413793)
## 28) smoothness_worst< -1.551775 8 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.551775 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.554151 70 4 B (0.05714286 0.94285714)
## 30) compactness_se>=-3.615179 16 4 B (0.25000000 0.75000000)
## 60) texture_mean>=2.935975 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.935975 13 1 B (0.07692308 0.92307692)
## 122) symmetry_worst< -1.998079 1 0 M (1.00000000 0.00000000) *
## 123) symmetry_worst>=-1.998079 12 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.615179 54 0 B (0.00000000 1.00000000) *
##
## $trees[[13]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) smoothness_mean>=-2.423454 652 318 M (0.51226994 0.48773006)
## 4) smoothness_mean< -2.312434 297 117 M (0.60606061 0.39393939)
## 8) symmetry_worst< -1.608146 236 69 M (0.70762712 0.29237288)
## 16) smoothness_worst>=-1.559798 179 38 M (0.78770950 0.21229050)
## 32) smoothness_mean>=-2.416986 167 26 M (0.84431138 0.15568862)
## 64) symmetry_worst>=-2.208456 163 22 M (0.86503067 0.13496933) *
## 65) symmetry_worst< -2.208456 4 0 B (0.00000000 1.00000000) *
## 33) smoothness_mean< -2.416986 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.559798 57 26 B (0.45614035 0.54385965)
## 34) texture_worst>=4.395741 38 12 M (0.68421053 0.31578947)
## 68) smoothness_worst>=-1.586874 29 5 M (0.82758621 0.17241379) *
## 69) smoothness_worst< -1.586874 9 2 B (0.22222222 0.77777778) *
## 35) texture_worst< 4.395741 19 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.608146 61 13 B (0.21311475 0.78688525)
## 18) texture_mean>=3.067477 15 5 M (0.66666667 0.33333333)
## 36) symmetry_worst>=-1.551105 11 1 M (0.90909091 0.09090909)
## 72) compactness_se>=-4.507761 10 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.507761 1 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.551105 4 0 B (0.00000000 1.00000000) *
## 19) texture_mean< 3.067477 46 3 B (0.06521739 0.93478261)
## 38) symmetry_worst>=-1.431522 7 3 B (0.42857143 0.57142857)
## 76) smoothness_mean>=-2.344658 2 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.344658 5 1 B (0.20000000 0.80000000) *
## 39) symmetry_worst< -1.431522 39 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.312434 355 154 B (0.43380282 0.56619718)
## 10) symmetry_worst>=-1.612559 127 46 M (0.63779528 0.36220472)
## 20) smoothness_worst>=-1.500061 96 18 M (0.81250000 0.18750000)
## 40) compactness_se>=-4.214968 92 14 M (0.84782609 0.15217391)
## 80) texture_mean>=2.822248 70 5 M (0.92857143 0.07142857) *
## 81) texture_mean< 2.822248 22 9 M (0.59090909 0.40909091) *
## 41) compactness_se< -4.214968 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.500061 31 3 B (0.09677419 0.90322581)
## 42) texture_mean>=3.137421 1 0 M (1.00000000 0.00000000) *
## 43) texture_mean< 3.137421 30 2 B (0.06666667 0.93333333)
## 86) smoothness_worst< -1.50756 7 2 B (0.28571429 0.71428571) *
## 87) smoothness_worst>=-1.50756 23 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.612559 228 73 B (0.32017544 0.67982456)
## 22) smoothness_worst< -1.567043 13 0 M (1.00000000 0.00000000) *
## 23) smoothness_worst>=-1.567043 215 60 B (0.27906977 0.72093023)
## 46) compactness_se>=-3.4389 52 20 M (0.61538462 0.38461538)
## 92) smoothness_mean< -2.25237 25 2 M (0.92000000 0.08000000) *
## 93) smoothness_mean>=-2.25237 27 9 B (0.33333333 0.66666667) *
## 47) compactness_se< -3.4389 163 28 B (0.17177914 0.82822086)
## 94) compactness_se>=-4.030876 113 27 B (0.23893805 0.76106195) *
## 95) compactness_se< -4.030876 50 1 B (0.02000000 0.98000000) *
## 3) smoothness_mean< -2.423454 260 76 B (0.29230769 0.70769231)
## 6) texture_mean>=2.921008 199 74 B (0.37185930 0.62814070)
## 12) texture_mean< 3.176386 142 68 B (0.47887324 0.52112676)
## 24) texture_mean>=3.130673 27 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.130673 115 41 B (0.35652174 0.64347826)
## 50) smoothness_worst< -1.60795 61 28 M (0.54098361 0.45901639)
## 100) symmetry_worst>=-1.874628 41 9 M (0.78048780 0.21951220) *
## 101) symmetry_worst< -1.874628 20 1 B (0.05000000 0.95000000) *
## 51) smoothness_worst>=-1.60795 54 8 B (0.14814815 0.85185185)
## 102) texture_mean< 2.930359 6 0 M (1.00000000 0.00000000) *
## 103) texture_mean>=2.930359 48 2 B (0.04166667 0.95833333) *
## 13) texture_mean>=3.176386 57 6 B (0.10526316 0.89473684)
## 26) texture_mean>=3.388429 8 3 M (0.62500000 0.37500000)
## 52) smoothness_mean>=-2.520061 5 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.520061 3 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 3.388429 49 1 B (0.02040816 0.97959184)
## 54) smoothness_mean>=-2.425205 1 0 M (1.00000000 0.00000000) *
## 55) smoothness_mean< -2.425205 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.921008 61 2 B (0.03278689 0.96721311)
## 14) texture_worst< 3.92417 4 1 B (0.25000000 0.75000000)
## 28) texture_mean>=2.707858 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.707858 3 0 B (0.00000000 1.00000000) *
## 15) texture_worst>=3.92417 57 1 B (0.01754386 0.98245614)
## 30) texture_worst< 4.400796 22 1 B (0.04545455 0.95454545)
## 60) texture_worst>=4.389974 1 0 M (1.00000000 0.00000000) *
## 61) texture_worst< 4.389974 21 0 B (0.00000000 1.00000000) *
## 31) texture_worst>=4.400796 35 0 B (0.00000000 1.00000000) *
##
## $trees[[14]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) texture_mean>=3.005187 425 172 M (0.59529412 0.40470588)
## 4) texture_mean< 3.02965 48 3 M (0.93750000 0.06250000)
## 8) compactness_se>=-4.262999 46 1 M (0.97826087 0.02173913)
## 16) smoothness_mean>=-2.60159 45 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.60159 1 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.262999 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.02965 377 169 M (0.55172414 0.44827586)
## 10) smoothness_mean>=-2.258569 81 18 M (0.77777778 0.22222222)
## 20) smoothness_mean< -2.099273 68 5 M (0.92647059 0.07352941)
## 40) compactness_se>=-4.045035 63 0 M (1.00000000 0.00000000) *
## 41) compactness_se< -4.045035 5 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.099273 13 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.258569 296 145 B (0.48986486 0.51013514)
## 22) compactness_se>=-3.477231 92 25 M (0.72826087 0.27173913)
## 44) smoothness_worst< -1.468038 73 7 M (0.90410959 0.09589041)
## 88) texture_mean>=3.038537 71 5 M (0.92957746 0.07042254) *
## 89) texture_mean< 3.038537 2 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.468038 19 1 B (0.05263158 0.94736842)
## 90) smoothness_mean>=-2.2833 1 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.2833 18 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.477231 204 78 B (0.38235294 0.61764706)
## 46) compactness_se< -3.872601 117 54 M (0.53846154 0.46153846)
## 92) smoothness_mean< -2.291157 96 36 M (0.62500000 0.37500000) *
## 93) smoothness_mean>=-2.291157 21 3 B (0.14285714 0.85714286) *
## 47) compactness_se>=-3.872601 87 15 B (0.17241379 0.82758621)
## 94) smoothness_worst>=-1.442386 6 0 M (1.00000000 0.00000000) *
## 95) smoothness_worst< -1.442386 81 9 B (0.11111111 0.88888889) *
## 3) texture_mean< 3.005187 487 183 B (0.37577002 0.62422998)
## 6) smoothness_worst>=-1.451541 87 32 M (0.63218391 0.36781609)
## 12) compactness_se>=-4.04059 65 15 M (0.76923077 0.23076923)
## 24) smoothness_worst< -1.349735 54 6 M (0.88888889 0.11111111)
## 48) texture_mean>=2.780541 49 3 M (0.93877551 0.06122449)
## 96) texture_worst< 4.783684 41 0 M (1.00000000 0.00000000) *
## 97) texture_worst>=4.783684 8 3 M (0.62500000 0.37500000) *
## 49) texture_mean< 2.780541 5 2 B (0.40000000 0.60000000)
## 98) smoothness_mean>=-2.150667 2 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean< -2.150667 3 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.349735 11 2 B (0.18181818 0.81818182)
## 50) symmetry_worst>=-1.232339 2 0 M (1.00000000 0.00000000) *
## 51) symmetry_worst< -1.232339 9 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.04059 22 5 B (0.22727273 0.77272727)
## 26) symmetry_worst< -1.750302 5 0 M (1.00000000 0.00000000) *
## 27) symmetry_worst>=-1.750302 17 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.451541 400 128 B (0.32000000 0.68000000)
## 14) texture_worst>=4.83005 10 1 M (0.90000000 0.10000000)
## 28) texture_mean>=2.915217 9 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.915217 1 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.83005 390 119 B (0.30512821 0.69487179)
## 30) smoothness_worst< -1.478565 314 110 B (0.35031847 0.64968153)
## 60) smoothness_worst>=-1.482701 24 3 M (0.87500000 0.12500000)
## 120) smoothness_mean< -2.253991 17 0 M (1.00000000 0.00000000) *
## 121) smoothness_mean>=-2.253991 7 3 M (0.57142857 0.42857143) *
## 61) smoothness_worst< -1.482701 290 89 B (0.30689655 0.69310345)
## 122) symmetry_worst< -1.692331 183 74 B (0.40437158 0.59562842) *
## 123) symmetry_worst>=-1.692331 107 15 B (0.14018692 0.85981308) *
## 31) smoothness_worst>=-1.478565 76 9 B (0.11842105 0.88157895)
## 62) texture_mean>=2.99172 4 0 M (1.00000000 0.00000000) *
## 63) texture_mean< 2.99172 72 5 B (0.06944444 0.93055556)
## 126) compactness_se>=-3.453499 14 4 B (0.28571429 0.71428571) *
## 127) compactness_se< -3.453499 58 1 B (0.01724138 0.98275862) *
##
## $trees[[15]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) smoothness_mean>=-2.489159 806 400 M (0.50372208 0.49627792)
## 4) symmetry_worst< -2.384404 25 3 M (0.88000000 0.12000000)
## 8) texture_mean>=2.861235 22 0 M (1.00000000 0.00000000) *
## 9) texture_mean< 2.861235 3 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-2.384404 781 384 B (0.49167734 0.50832266)
## 10) symmetry_worst>=-2.232873 745 363 M (0.51275168 0.48724832)
## 20) smoothness_mean< -2.473552 21 2 M (0.90476190 0.09523810)
## 40) texture_mean>=2.967697 19 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.967697 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.473552 724 361 M (0.50138122 0.49861878)
## 42) smoothness_mean>=-2.425205 646 305 M (0.52786378 0.47213622)
## 84) texture_worst>=4.896309 113 35 M (0.69026549 0.30973451) *
## 85) texture_worst< 4.896309 533 263 B (0.49343340 0.50656660) *
## 43) smoothness_mean< -2.425205 78 22 B (0.28205128 0.71794872)
## 86) smoothness_mean< -2.441446 49 22 B (0.44897959 0.55102041) *
## 87) smoothness_mean>=-2.441446 29 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -2.232873 36 2 B (0.05555556 0.94444444)
## 22) smoothness_mean< -2.453321 3 1 M (0.66666667 0.33333333)
## 44) texture_mean>=3.037949 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.037949 1 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.453321 33 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.489159 106 23 B (0.21698113 0.78301887)
## 6) symmetry_worst>=-1.667161 27 13 M (0.51851852 0.48148148)
## 12) smoothness_worst< -1.616835 15 1 M (0.93333333 0.06666667)
## 24) texture_mean< 3.135016 14 0 M (1.00000000 0.00000000) *
## 25) texture_mean>=3.135016 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.616835 12 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.667161 79 9 B (0.11392405 0.88607595)
## 14) compactness_se>=-3.613485 21 7 B (0.33333333 0.66666667)
## 28) smoothness_mean>=-2.508983 3 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean< -2.508983 18 4 B (0.22222222 0.77777778)
## 58) texture_mean>=3.076827 5 1 M (0.80000000 0.20000000)
## 116) texture_mean< 3.103494 4 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 59) texture_mean< 3.076827 13 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.613485 58 2 B (0.03448276 0.96551724)
## 30) compactness_se< -4.692873 11 2 B (0.18181818 0.81818182)
## 60) compactness_se>=-4.711555 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.711555 9 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.692873 47 0 B (0.00000000 1.00000000) *
##
## $trees[[16]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 399 M (0.56250000 0.43750000)
## 2) compactness_se>=-4.691273 882 370 M (0.58049887 0.41950113)
## 4) smoothness_mean>=-2.332942 448 150 M (0.66517857 0.33482143)
## 8) compactness_se>=-4.032549 358 96 M (0.73184358 0.26815642)
## 16) symmetry_worst>=-2.188127 337 80 M (0.76261128 0.23738872)
## 32) smoothness_mean< -2.106197 314 64 M (0.79617834 0.20382166)
## 64) texture_worst>=4.895983 58 0 M (1.00000000 0.00000000) *
## 65) texture_worst< 4.895983 256 64 M (0.75000000 0.25000000) *
## 33) smoothness_mean>=-2.106197 23 7 B (0.30434783 0.69565217)
## 66) symmetry_worst>=-1.596878 8 1 M (0.87500000 0.12500000) *
## 67) symmetry_worst< -1.596878 15 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -2.188127 21 5 B (0.23809524 0.76190476)
## 34) smoothness_mean>=-2.244441 5 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.244441 16 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.032549 90 36 B (0.40000000 0.60000000)
## 18) smoothness_mean< -2.291157 40 6 M (0.85000000 0.15000000)
## 36) texture_mean>=2.834088 37 3 M (0.91891892 0.08108108)
## 72) compactness_se< -4.098353 36 2 M (0.94444444 0.05555556) *
## 73) compactness_se>=-4.098353 1 0 B (0.00000000 1.00000000) *
## 37) texture_mean< 2.834088 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.291157 50 2 B (0.04000000 0.96000000)
## 38) smoothness_mean>=-2.21595 12 2 B (0.16666667 0.83333333)
## 76) compactness_se< -4.208747 2 0 M (1.00000000 0.00000000) *
## 77) compactness_se>=-4.208747 10 0 B (0.00000000 1.00000000) *
## 39) smoothness_mean< -2.21595 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.332942 434 214 B (0.49308756 0.50691244)
## 10) texture_mean< 3.227241 376 171 M (0.54521277 0.45478723)
## 20) compactness_se< -3.426516 304 118 M (0.61184211 0.38815789)
## 40) texture_mean>=2.874407 247 79 M (0.68016194 0.31983806)
## 80) compactness_se< -4.039628 134 23 M (0.82835821 0.17164179) *
## 81) compactness_se>=-4.039628 113 56 M (0.50442478 0.49557522) *
## 41) texture_mean< 2.874407 57 18 B (0.31578947 0.68421053)
## 82) smoothness_worst>=-1.454595 8 1 M (0.87500000 0.12500000) *
## 83) smoothness_worst< -1.454595 49 11 B (0.22448980 0.77551020) *
## 21) compactness_se>=-3.426516 72 19 B (0.26388889 0.73611111)
## 42) texture_mean>=3.06339 26 10 M (0.61538462 0.38461538)
## 84) symmetry_worst>=-2.189138 20 4 M (0.80000000 0.20000000) *
## 85) symmetry_worst< -2.189138 6 0 B (0.00000000 1.00000000) *
## 43) texture_mean< 3.06339 46 3 B (0.06521739 0.93478261)
## 86) compactness_se< -3.392487 6 2 B (0.33333333 0.66666667) *
## 87) compactness_se>=-3.392487 40 1 B (0.02500000 0.97500000) *
## 11) texture_mean>=3.227241 58 9 B (0.15517241 0.84482759)
## 22) compactness_se>=-3.482708 6 2 M (0.66666667 0.33333333)
## 44) texture_mean>=3.256167 4 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.256167 2 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.482708 52 5 B (0.09615385 0.90384615)
## 46) texture_mean>=3.431382 2 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.431382 50 3 B (0.06000000 0.94000000)
## 94) texture_mean>=3.388429 6 2 B (0.33333333 0.66666667) *
## 95) texture_mean< 3.388429 44 1 B (0.02272727 0.97727273) *
## 3) compactness_se< -4.691273 30 1 B (0.03333333 0.96666667)
## 6) symmetry_worst>=-1.124659 1 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.124659 29 0 B (0.00000000 1.00000000) *
##
## $trees[[17]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) smoothness_mean>=-2.423737 712 329 M (0.53792135 0.46207865)
## 4) compactness_se>=-3.011681 66 11 M (0.83333333 0.16666667)
## 8) smoothness_worst< -1.454202 48 1 M (0.97916667 0.02083333)
## 16) smoothness_mean>=-2.336585 43 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.336585 5 1 M (0.80000000 0.20000000)
## 34) texture_mean>=2.972459 4 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 2.972459 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.454202 18 8 B (0.44444444 0.55555556)
## 18) smoothness_mean>=-2.161865 6 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean< -2.161865 12 2 B (0.16666667 0.83333333)
## 38) symmetry_worst< -1.642275 2 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst>=-1.642275 10 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.011681 646 318 M (0.50773994 0.49226006)
## 10) smoothness_worst>=-1.472307 191 67 M (0.64921466 0.35078534)
## 20) smoothness_mean< -2.300091 57 5 M (0.91228070 0.08771930)
## 40) texture_mean>=2.735974 54 2 M (0.96296296 0.03703704)
## 80) compactness_se>=-4.497673 53 1 M (0.98113208 0.01886792) *
## 81) compactness_se< -4.497673 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.735974 3 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.300091 134 62 M (0.53731343 0.46268657)
## 42) compactness_se>=-4.030558 98 30 M (0.69387755 0.30612245)
## 84) texture_mean>=2.915043 60 7 M (0.88333333 0.11666667) *
## 85) texture_mean< 2.915043 38 15 B (0.39473684 0.60526316) *
## 43) compactness_se< -4.030558 36 4 B (0.11111111 0.88888889)
## 86) symmetry_worst< -1.743442 7 3 M (0.57142857 0.42857143) *
## 87) symmetry_worst>=-1.743442 29 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.472307 455 204 B (0.44835165 0.55164835)
## 22) symmetry_worst< -1.692015 327 150 M (0.54128440 0.45871560)
## 44) smoothness_worst< -1.474843 302 126 M (0.58278146 0.41721854)
## 88) texture_mean< 3.36829 288 112 M (0.61111111 0.38888889) *
## 89) texture_mean>=3.36829 14 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.474843 25 1 B (0.04000000 0.96000000)
## 90) texture_mean>=2.978826 1 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.978826 24 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.692015 128 27 B (0.21093750 0.78906250)
## 46) compactness_se>=-3.4704 20 8 M (0.60000000 0.40000000)
## 92) smoothness_worst>=-1.51165 11 1 M (0.90909091 0.09090909) *
## 93) smoothness_worst< -1.51165 9 2 B (0.22222222 0.77777778) *
## 47) compactness_se< -3.4704 108 15 B (0.13888889 0.86111111)
## 94) texture_worst>=4.818867 3 0 M (1.00000000 0.00000000) *
## 95) texture_worst< 4.818867 105 12 B (0.11428571 0.88571429) *
## 3) smoothness_mean< -2.423737 200 65 B (0.32500000 0.67500000)
## 6) texture_mean< 3.176386 164 65 B (0.39634146 0.60365854)
## 12) smoothness_worst>=-1.656234 126 63 M (0.50000000 0.50000000)
## 24) texture_mean>=3.111958 16 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 3.111958 110 47 B (0.42727273 0.57272727)
## 50) smoothness_worst< -1.551775 86 39 M (0.54651163 0.45348837)
## 100) smoothness_mean< -2.432353 76 29 M (0.61842105 0.38157895) *
## 101) smoothness_mean>=-2.432353 10 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.551775 24 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.656234 38 2 B (0.05263158 0.94736842)
## 26) smoothness_worst< -1.720903 7 2 B (0.28571429 0.71428571)
## 52) compactness_se>=-3.013033 2 0 M (1.00000000 0.00000000) *
## 53) compactness_se< -3.013033 5 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.720903 31 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=3.176386 36 0 B (0.00000000 1.00000000) *
##
## $trees[[18]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 401 B (0.43969298 0.56030702)
## 2) smoothness_worst>=-1.603315 769 369 B (0.47984395 0.52015605)
## 4) texture_mean>=2.709047 735 367 B (0.49931973 0.50068027)
## 8) compactness_se>=-3.719548 299 118 M (0.60535117 0.39464883)
## 16) texture_mean< 2.746628 28 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=2.746628 271 118 M (0.56457565 0.43542435)
## 34) symmetry_worst>=-1.606092 73 17 M (0.76712329 0.23287671)
## 68) smoothness_mean>=-2.298098 50 5 M (0.90000000 0.10000000) *
## 69) smoothness_mean< -2.298098 23 11 B (0.47826087 0.52173913) *
## 35) symmetry_worst< -1.606092 198 97 B (0.48989899 0.51010101)
## 70) smoothness_mean< -2.14559 180 83 M (0.53888889 0.46111111) *
## 71) smoothness_mean>=-2.14559 18 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.719548 436 186 B (0.42660550 0.57339450)
## 18) compactness_se< -3.859436 366 178 B (0.48633880 0.51366120)
## 36) smoothness_mean< -2.291157 278 122 M (0.56115108 0.43884892)
## 72) smoothness_worst>=-1.55307 192 62 M (0.67708333 0.32291667) *
## 73) smoothness_worst< -1.55307 86 26 B (0.30232558 0.69767442) *
## 37) smoothness_mean>=-2.291157 88 22 B (0.25000000 0.75000000)
## 74) compactness_se>=-4.032549 37 15 M (0.59459459 0.40540541) *
## 75) compactness_se< -4.032549 51 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-3.859436 70 8 B (0.11428571 0.88571429)
## 38) smoothness_worst>=-1.472895 9 3 M (0.66666667 0.33333333)
## 76) smoothness_mean< -2.17953 6 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean>=-2.17953 3 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst< -1.472895 61 2 B (0.03278689 0.96721311)
## 78) symmetry_worst< -1.901985 8 2 B (0.25000000 0.75000000) *
## 79) symmetry_worst>=-1.901985 53 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.709047 34 2 B (0.05882353 0.94117647)
## 10) texture_worst< 3.80118 10 2 B (0.20000000 0.80000000)
## 20) texture_mean>=2.630644 2 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.630644 8 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=3.80118 24 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.603315 143 32 B (0.22377622 0.77622378)
## 6) symmetry_worst>=-1.868413 63 28 B (0.44444444 0.55555556)
## 12) texture_worst>=4.334485 50 22 M (0.56000000 0.44000000)
## 24) texture_mean< 3.062357 33 7 M (0.78787879 0.21212121)
## 48) texture_mean>=2.939162 28 2 M (0.92857143 0.07142857)
## 96) compactness_se>=-4.938351 27 1 M (0.96296296 0.03703704) *
## 97) compactness_se< -4.938351 1 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.939162 5 0 B (0.00000000 1.00000000) *
## 25) texture_mean>=3.062357 17 2 B (0.11764706 0.88235294)
## 50) smoothness_mean>=-2.337942 2 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.337942 15 0 B (0.00000000 1.00000000) *
## 13) texture_worst< 4.334485 13 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.868413 80 4 B (0.05000000 0.95000000)
## 14) smoothness_mean>=-2.373736 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.373736 79 3 B (0.03797468 0.96202532)
## 30) smoothness_worst< -1.718904 10 3 B (0.30000000 0.70000000)
## 60) compactness_se>=-3.013033 3 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -3.013033 7 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.718904 69 0 B (0.00000000 1.00000000) *
##
## $trees[[19]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 353 B (0.38706140 0.61293860)
## 2) symmetry_worst< -2.385442 27 3 M (0.88888889 0.11111111)
## 4) texture_mean< 3.283931 26 2 M (0.92307692 0.07692308)
## 8) smoothness_worst< -1.534654 21 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst>=-1.534654 5 2 M (0.60000000 0.40000000)
## 18) texture_mean>=3.050671 3 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.050671 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.283931 1 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.385442 885 329 B (0.37175141 0.62824859)
## 6) symmetry_worst>=-1.840831 498 217 B (0.43574297 0.56425703)
## 12) symmetry_worst< -1.750623 159 70 M (0.55974843 0.44025157)
## 24) smoothness_worst>=-1.547262 101 30 M (0.70297030 0.29702970)
## 48) smoothness_mean< -2.210016 85 15 M (0.82352941 0.17647059)
## 96) compactness_se>=-4.388189 81 11 M (0.86419753 0.13580247) *
## 97) compactness_se< -4.388189 4 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.210016 16 1 B (0.06250000 0.93750000)
## 98) compactness_se>=-3.317826 1 0 M (1.00000000 0.00000000) *
## 99) compactness_se< -3.317826 15 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.547262 58 18 B (0.31034483 0.68965517)
## 50) texture_worst>=4.56463 12 1 M (0.91666667 0.08333333)
## 100) texture_mean< 3.176386 11 0 M (1.00000000 0.00000000) *
## 101) texture_mean>=3.176386 1 0 B (0.00000000 1.00000000) *
## 51) texture_worst< 4.56463 46 7 B (0.15217391 0.84782609)
## 102) smoothness_mean>=-2.302636 4 0 M (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.302636 42 3 B (0.07142857 0.92857143) *
## 13) symmetry_worst>=-1.750623 339 128 B (0.37758112 0.62241888)
## 26) smoothness_worst>=-1.473088 100 41 M (0.59000000 0.41000000)
## 52) compactness_se< -2.961809 85 28 M (0.67058824 0.32941176)
## 104) symmetry_worst>=-1.65458 67 14 M (0.79104478 0.20895522) *
## 105) symmetry_worst< -1.65458 18 4 B (0.22222222 0.77777778) *
## 53) compactness_se>=-2.961809 15 2 B (0.13333333 0.86666667)
## 106) texture_mean< 2.81718 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean>=2.81718 14 1 B (0.07142857 0.92857143) *
## 27) smoothness_worst< -1.473088 239 69 B (0.28870293 0.71129707)
## 54) compactness_se>=-3.483184 52 20 M (0.61538462 0.38461538)
## 108) texture_mean>=2.956366 37 7 M (0.81081081 0.18918919) *
## 109) texture_mean< 2.956366 15 2 B (0.13333333 0.86666667) *
## 55) compactness_se< -3.483184 187 37 B (0.19786096 0.80213904)
## 110) compactness_se< -4.658767 5 0 M (1.00000000 0.00000000) *
## 111) compactness_se>=-4.658767 182 32 B (0.17582418 0.82417582) *
## 7) symmetry_worst< -1.840831 387 112 B (0.28940568 0.71059432)
## 14) texture_worst>=4.907333 79 34 M (0.56962025 0.43037975)
## 28) symmetry_worst>=-2.207988 54 12 M (0.77777778 0.22222222)
## 56) smoothness_mean>=-2.427815 34 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.427815 20 8 B (0.40000000 0.60000000)
## 114) texture_worst< 4.987149 8 0 M (1.00000000 0.00000000) *
## 115) texture_worst>=4.987149 12 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -2.207988 25 3 B (0.12000000 0.88000000)
## 58) compactness_se>=-3.413706 3 0 M (1.00000000 0.00000000) *
## 59) compactness_se< -3.413706 22 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.907333 308 67 B (0.21753247 0.78246753)
## 30) compactness_se>=-3.02233 20 7 M (0.65000000 0.35000000)
## 60) compactness_se< -2.870592 12 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-2.870592 8 1 B (0.12500000 0.87500000)
## 122) texture_mean>=3.109826 1 0 M (1.00000000 0.00000000) *
## 123) texture_mean< 3.109826 7 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.02233 288 54 B (0.18750000 0.81250000)
## 62) smoothness_mean< -2.35264 140 44 B (0.31428571 0.68571429)
## 124) smoothness_mean>=-2.394871 44 13 M (0.70454545 0.29545455) *
## 125) smoothness_mean< -2.394871 96 13 B (0.13541667 0.86458333) *
## 63) smoothness_mean>=-2.35264 148 10 B (0.06756757 0.93243243)
## 126) smoothness_worst>=-1.49704 73 10 B (0.13698630 0.86301370) *
## 127) smoothness_worst< -1.49704 75 0 B (0.00000000 1.00000000) *
##
## $trees[[20]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 403 B (0.44188596 0.55811404)
## 2) symmetry_worst>=-1.353976 49 10 M (0.79591837 0.20408163)
## 4) texture_mean>=2.644674 46 7 M (0.84782609 0.15217391)
## 8) compactness_se< -2.588521 41 3 M (0.92682927 0.07317073)
## 16) texture_mean< 3.104075 34 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.104075 7 3 M (0.57142857 0.42857143)
## 34) texture_mean>=3.116842 4 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.116842 3 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.588521 5 1 B (0.20000000 0.80000000)
## 18) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.915767 4 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.644674 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.353976 863 364 B (0.42178447 0.57821553)
## 6) compactness_se< -3.355844 757 343 B (0.45310436 0.54689564)
## 12) compactness_se>=-3.721197 239 94 M (0.60669456 0.39330544)
## 24) texture_worst< 4.616724 154 41 M (0.73376623 0.26623377)
## 48) smoothness_mean>=-2.322902 77 6 M (0.92207792 0.07792208)
## 96) texture_worst>=4.050785 65 1 M (0.98461538 0.01538462) *
## 97) texture_worst< 4.050785 12 5 M (0.58333333 0.41666667) *
## 49) smoothness_mean< -2.322902 77 35 M (0.54545455 0.45454545)
## 98) texture_worst>=4.56463 20 0 M (1.00000000 0.00000000) *
## 99) texture_worst< 4.56463 57 22 B (0.38596491 0.61403509) *
## 25) texture_worst>=4.616724 85 32 B (0.37647059 0.62352941)
## 50) smoothness_mean>=-2.286719 19 4 M (0.78947368 0.21052632)
## 100) smoothness_mean< -2.119611 15 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean>=-2.119611 4 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean< -2.286719 66 17 B (0.25757576 0.74242424)
## 102) smoothness_mean< -2.349943 30 15 M (0.50000000 0.50000000) *
## 103) smoothness_mean>=-2.349943 36 2 B (0.05555556 0.94444444) *
## 13) compactness_se< -3.721197 518 198 B (0.38223938 0.61776062)
## 26) compactness_se>=-4.705565 488 198 B (0.40573770 0.59426230)
## 52) compactness_se< -4.116284 249 118 M (0.52610442 0.47389558)
## 104) texture_mean< 3.032503 168 61 M (0.63690476 0.36309524) *
## 105) texture_mean>=3.032503 81 24 B (0.29629630 0.70370370) *
## 53) compactness_se>=-4.116284 239 67 B (0.28033473 0.71966527)
## 106) texture_worst>=4.895983 46 17 M (0.63043478 0.36956522) *
## 107) texture_worst< 4.895983 193 38 B (0.19689119 0.80310881) *
## 27) compactness_se< -4.705565 30 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.355844 106 21 B (0.19811321 0.80188679)
## 14) texture_mean>=3.038537 33 16 B (0.48484848 0.51515152)
## 28) texture_mean< 3.216873 20 6 M (0.70000000 0.30000000)
## 56) smoothness_mean< -2.154474 17 3 M (0.82352941 0.17647059)
## 112) symmetry_worst>=-2.143533 13 0 M (1.00000000 0.00000000) *
## 113) symmetry_worst< -2.143533 4 1 B (0.25000000 0.75000000) *
## 57) smoothness_mean>=-2.154474 3 0 B (0.00000000 1.00000000) *
## 29) texture_mean>=3.216873 13 2 B (0.15384615 0.84615385)
## 58) texture_mean>=3.252756 2 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.252756 11 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.038537 73 5 B (0.06849315 0.93150685)
## 30) smoothness_mean>=-2.082188 3 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.082188 70 2 B (0.02857143 0.97142857)
## 62) smoothness_worst>=-1.481325 15 2 B (0.13333333 0.86666667)
## 124) texture_mean>=3.031099 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.031099 14 1 B (0.07142857 0.92857143) *
## 63) smoothness_worst< -1.481325 55 0 B (0.00000000 1.00000000) *
##
## $trees[[21]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 387 B (0.42434211 0.57565789)
## 2) symmetry_worst>=-1.366937 66 10 M (0.84848485 0.15151515)
## 4) smoothness_mean< -2.036051 64 8 M (0.87500000 0.12500000)
## 8) compactness_se< -2.588521 61 6 M (0.90163934 0.09836066)
## 16) texture_mean< 3.104075 50 2 M (0.96000000 0.04000000)
## 32) smoothness_mean>=-2.352085 34 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.352085 16 2 M (0.87500000 0.12500000)
## 66) texture_mean>=2.89093 14 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.89093 2 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.104075 11 4 M (0.63636364 0.36363636)
## 34) texture_mean>=3.116842 7 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.116842 4 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.588521 3 1 B (0.33333333 0.66666667)
## 18) texture_mean>=2.996569 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.996569 2 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.036051 2 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 846 331 B (0.39125296 0.60874704)
## 6) texture_mean>=3.388429 17 2 M (0.88235294 0.11764706)
## 12) compactness_se>=-4.317414 15 0 M (1.00000000 0.00000000) *
## 13) compactness_se< -4.317414 2 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 3.388429 829 316 B (0.38118215 0.61881785)
## 14) smoothness_mean>=-2.423454 627 265 B (0.42264753 0.57735247)
## 28) symmetry_worst>=-1.786753 307 153 M (0.50162866 0.49837134)
## 56) symmetry_worst< -1.781339 23 0 M (1.00000000 0.00000000) *
## 57) symmetry_worst>=-1.781339 284 131 B (0.46126761 0.53873239)
## 114) compactness_se>=-3.703794 110 42 M (0.61818182 0.38181818) *
## 115) compactness_se< -3.703794 174 63 B (0.36206897 0.63793103) *
## 29) symmetry_worst< -1.786753 320 111 B (0.34687500 0.65312500)
## 58) symmetry_worst< -1.938643 177 78 B (0.44067797 0.55932203)
## 116) symmetry_worst>=-1.964873 37 6 M (0.83783784 0.16216216) *
## 117) symmetry_worst< -1.964873 140 47 B (0.33571429 0.66428571) *
## 59) symmetry_worst>=-1.938643 143 33 B (0.23076923 0.76923077)
## 118) texture_mean< 2.724206 14 4 M (0.71428571 0.28571429) *
## 119) texture_mean>=2.724206 129 23 B (0.17829457 0.82170543) *
## 15) smoothness_mean< -2.423454 202 51 B (0.25247525 0.74752475)
## 30) symmetry_worst>=-1.541072 25 8 M (0.68000000 0.32000000)
## 60) smoothness_mean< -2.431217 21 4 M (0.80952381 0.19047619)
## 120) texture_mean>=2.973641 10 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.973641 11 4 M (0.63636364 0.36363636) *
## 61) smoothness_mean>=-2.431217 4 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.541072 177 34 B (0.19209040 0.80790960)
## 62) smoothness_mean< -2.467991 111 30 B (0.27027027 0.72972973)
## 124) smoothness_mean>=-2.468227 7 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.468227 104 23 B (0.22115385 0.77884615) *
## 63) smoothness_mean>=-2.467991 66 4 B (0.06060606 0.93939394)
## 126) symmetry_worst< -2.004084 19 4 B (0.21052632 0.78947368) *
## 127) symmetry_worst>=-2.004084 47 0 B (0.00000000 1.00000000) *
##
## $trees[[22]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 B (0.47587719 0.52412281)
## 2) texture_mean>=2.708379 867 426 B (0.49134948 0.50865052)
## 4) smoothness_worst>=-1.501069 363 155 M (0.57300275 0.42699725)
## 8) smoothness_worst< -1.476605 142 32 M (0.77464789 0.22535211)
## 16) smoothness_worst>=-1.482699 75 6 M (0.92000000 0.08000000)
## 32) texture_worst>=4.126187 73 4 M (0.94520548 0.05479452)
## 64) texture_worst< 4.635614 63 0 M (1.00000000 0.00000000) *
## 65) texture_worst>=4.635614 10 4 M (0.60000000 0.40000000) *
## 33) texture_worst< 4.126187 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.482699 67 26 M (0.61194030 0.38805970)
## 34) smoothness_worst< -1.484675 57 16 M (0.71929825 0.28070175)
## 68) texture_worst>=4.484566 48 8 M (0.83333333 0.16666667) *
## 69) texture_worst< 4.484566 9 1 B (0.11111111 0.88888889) *
## 35) smoothness_worst>=-1.484675 10 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.476605 221 98 B (0.44343891 0.55656109)
## 18) smoothness_worst>=-1.473476 192 95 B (0.49479167 0.50520833)
## 36) compactness_se>=-4.032549 128 51 M (0.60156250 0.39843750)
## 72) compactness_se< -3.532908 60 8 M (0.86666667 0.13333333) *
## 73) compactness_se>=-3.532908 68 25 B (0.36764706 0.63235294) *
## 37) compactness_se< -4.032549 64 18 B (0.28125000 0.71875000)
## 74) texture_mean>=3.07984 14 5 M (0.64285714 0.35714286) *
## 75) texture_mean< 3.07984 50 9 B (0.18000000 0.82000000) *
## 19) smoothness_worst< -1.473476 29 3 B (0.10344828 0.89655172)
## 38) texture_mean>=3.069079 3 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 3.069079 26 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.501069 504 218 B (0.43253968 0.56746032)
## 10) smoothness_worst< -1.519464 451 210 B (0.46563193 0.53436807)
## 20) smoothness_worst>=-1.520292 20 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.520292 431 190 B (0.44083527 0.55916473)
## 42) smoothness_worst< -1.533868 375 180 B (0.48000000 0.52000000)
## 84) texture_worst>=5.093455 43 10 M (0.76744186 0.23255814) *
## 85) texture_worst< 5.093455 332 147 B (0.44277108 0.55722892) *
## 43) smoothness_worst>=-1.533868 56 10 B (0.17857143 0.82142857)
## 86) texture_mean>=3.065024 20 9 B (0.45000000 0.55000000) *
## 87) texture_mean< 3.065024 36 1 B (0.02777778 0.97222222) *
## 11) smoothness_worst>=-1.519464 53 8 B (0.15094340 0.84905660)
## 22) texture_mean>=3.006423 25 8 B (0.32000000 0.68000000)
## 44) symmetry_worst< -1.551134 9 2 M (0.77777778 0.22222222)
## 88) texture_worst>=4.577291 7 0 M (1.00000000 0.00000000) *
## 89) texture_worst< 4.577291 2 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.551134 16 1 B (0.06250000 0.93750000)
## 90) smoothness_mean>=-2.263106 1 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.263106 15 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.006423 28 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.708379 45 8 B (0.17777778 0.82222222)
## 6) texture_mean< 2.479051 6 1 M (0.83333333 0.16666667)
## 12) smoothness_mean>=-2.170026 5 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.170026 1 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=2.479051 39 3 B (0.07692308 0.92307692)
## 14) symmetry_worst>=-1.112025 2 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst< -1.112025 37 1 B (0.02702703 0.97297297)
## 30) texture_mean>=2.648549 10 1 B (0.10000000 0.90000000)
## 60) texture_mean< 2.666527 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.666527 9 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 2.648549 27 0 B (0.00000000 1.00000000) *
##
## $trees[[23]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 426 M (0.53289474 0.46710526)
## 2) symmetry_worst>=-1.293329 35 3 M (0.91428571 0.08571429)
## 4) smoothness_worst>=-1.49848 29 1 M (0.96551724 0.03448276)
## 8) smoothness_mean>=-2.340816 28 0 M (1.00000000 0.00000000) *
## 9) smoothness_mean< -2.340816 1 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.49848 6 2 M (0.66666667 0.33333333)
## 10) smoothness_mean< -2.349786 4 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.349786 2 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.293329 877 423 M (0.51767389 0.48232611)
## 6) texture_mean>=2.653549 846 396 M (0.53191489 0.46808511)
## 12) texture_mean< 3.227241 785 354 M (0.54904459 0.45095541)
## 24) texture_worst>=5.032208 40 5 M (0.87500000 0.12500000)
## 48) texture_worst< 5.280287 35 0 M (1.00000000 0.00000000) *
## 49) texture_worst>=5.280287 5 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 5.032208 745 349 M (0.53154362 0.46845638)
## 50) smoothness_worst< -1.403628 702 316 M (0.54985755 0.45014245)
## 100) compactness_se< -3.392487 619 262 M (0.57673667 0.42326333) *
## 101) compactness_se>=-3.392487 83 29 B (0.34939759 0.65060241) *
## 51) smoothness_worst>=-1.403628 43 10 B (0.23255814 0.76744186)
## 102) compactness_se>=-3.217781 12 3 M (0.75000000 0.25000000) *
## 103) compactness_se< -3.217781 31 1 B (0.03225806 0.96774194) *
## 13) texture_mean>=3.227241 61 19 B (0.31147541 0.68852459)
## 26) smoothness_worst< -1.582589 12 2 M (0.83333333 0.16666667)
## 52) texture_mean>=3.331484 10 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 3.331484 2 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.582589 49 9 B (0.18367347 0.81632653)
## 54) compactness_se>=-3.575987 7 2 M (0.71428571 0.28571429)
## 108) compactness_se< -2.831802 5 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-2.831802 2 0 B (0.00000000 1.00000000) *
## 55) compactness_se< -3.575987 42 4 B (0.09523810 0.90476190)
## 110) smoothness_mean>=-2.306298 2 0 M (1.00000000 0.00000000) *
## 111) smoothness_mean< -2.306298 40 2 B (0.05000000 0.95000000) *
## 7) texture_mean< 2.653549 31 4 B (0.12903226 0.87096774)
## 14) smoothness_mean>=-2.07745 7 3 M (0.57142857 0.42857143)
## 28) texture_mean< 2.515298 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.515298 3 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.07745 24 0 B (0.00000000 1.00000000) *
##
## $trees[[24]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 428 B (0.46929825 0.53070175)
## 2) symmetry_worst>=-1.348749 42 6 M (0.85714286 0.14285714)
## 4) texture_mean>=2.756192 36 2 M (0.94444444 0.05555556)
## 8) texture_worst>=4.373597 26 0 M (1.00000000 0.00000000) *
## 9) texture_worst< 4.373597 10 2 M (0.80000000 0.20000000)
## 18) texture_worst< 4.279513 8 0 M (1.00000000 0.00000000) *
## 19) texture_worst>=4.279513 2 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.756192 6 2 B (0.33333333 0.66666667)
## 10) compactness_se>=-3.3026 2 0 M (1.00000000 0.00000000) *
## 11) compactness_se< -3.3026 4 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.348749 870 392 B (0.45057471 0.54942529)
## 6) compactness_se>=-4.705732 850 392 B (0.46117647 0.53882353)
## 12) texture_worst< 4.54138 369 172 M (0.53387534 0.46612466)
## 24) texture_worst>=4.523593 41 6 M (0.85365854 0.14634146)
## 48) smoothness_mean< -2.234468 31 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.234468 10 4 B (0.40000000 0.60000000)
## 98) texture_mean>=3.023554 4 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.023554 6 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.523593 328 162 B (0.49390244 0.50609756)
## 50) smoothness_worst< -1.541278 152 57 M (0.62500000 0.37500000)
## 100) compactness_se>=-4.501722 126 36 M (0.71428571 0.28571429) *
## 101) compactness_se< -4.501722 26 5 B (0.19230769 0.80769231) *
## 51) smoothness_worst>=-1.541278 176 67 B (0.38068182 0.61931818)
## 102) smoothness_worst>=-1.483493 102 47 M (0.53921569 0.46078431) *
## 103) smoothness_worst< -1.483493 74 12 B (0.16216216 0.83783784) *
## 13) texture_worst>=4.54138 481 195 B (0.40540541 0.59459459)
## 26) compactness_se>=-3.334337 56 16 M (0.71428571 0.28571429)
## 52) smoothness_mean>=-2.41714 37 4 M (0.89189189 0.10810811)
## 104) texture_mean>=3.03709 31 0 M (1.00000000 0.00000000) *
## 105) texture_mean< 3.03709 6 2 B (0.33333333 0.66666667) *
## 53) smoothness_mean< -2.41714 19 7 B (0.36842105 0.63157895)
## 106) compactness_se< -3.106177 7 0 M (1.00000000 0.00000000) *
## 107) compactness_se>=-3.106177 12 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.334337 425 155 B (0.36470588 0.63529412)
## 54) smoothness_mean< -2.351049 235 111 B (0.47234043 0.52765957)
## 108) smoothness_mean>=-2.367605 23 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.367605 212 88 B (0.41509434 0.58490566) *
## 55) smoothness_mean>=-2.351049 190 44 B (0.23157895 0.76842105)
## 110) texture_worst>=5.277564 9 0 M (1.00000000 0.00000000) *
## 111) texture_worst< 5.277564 181 35 B (0.19337017 0.80662983) *
## 7) compactness_se< -4.705732 20 0 B (0.00000000 1.00000000) *
##
## $trees[[25]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_mean>=2.892591 632 263 M (0.58386076 0.41613924)
## 4) compactness_se< -4.094455 216 63 M (0.70833333 0.29166667)
## 8) smoothness_mean>=-2.426508 128 23 M (0.82031250 0.17968750)
## 16) smoothness_mean< -2.295141 113 12 M (0.89380531 0.10619469)
## 32) texture_mean< 3.227241 106 7 M (0.93396226 0.06603774)
## 64) texture_worst>=4.35485 104 5 M (0.95192308 0.04807692) *
## 65) texture_worst< 4.35485 2 0 B (0.00000000 1.00000000) *
## 33) texture_mean>=3.227241 7 2 B (0.28571429 0.71428571)
## 66) compactness_se>=-4.299856 2 0 M (1.00000000 0.00000000) *
## 67) compactness_se< -4.299856 5 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.295141 15 4 B (0.26666667 0.73333333)
## 34) smoothness_mean>=-2.222419 4 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.222419 11 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.426508 88 40 M (0.54545455 0.45454545)
## 18) symmetry_worst>=-1.705164 59 17 M (0.71186441 0.28813559)
## 36) smoothness_worst< -1.567962 37 2 M (0.94594595 0.05405405)
## 72) texture_mean>=2.958609 35 0 M (1.00000000 0.00000000) *
## 73) texture_mean< 2.958609 2 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst>=-1.567962 22 7 B (0.31818182 0.68181818)
## 74) texture_mean< 2.936149 7 0 M (1.00000000 0.00000000) *
## 75) texture_mean>=2.936149 15 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.705164 29 6 B (0.20689655 0.79310345)
## 38) texture_worst>=4.89091 12 6 M (0.50000000 0.50000000)
## 76) texture_worst< 4.984007 6 0 M (1.00000000 0.00000000) *
## 77) texture_worst>=4.984007 6 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 4.89091 17 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-4.094455 416 200 M (0.51923077 0.48076923)
## 10) smoothness_mean>=-2.28279 148 39 M (0.73648649 0.26351351)
## 20) compactness_se>=-4.030876 139 30 M (0.78417266 0.21582734)
## 40) texture_mean>=2.912343 131 24 M (0.81679389 0.18320611)
## 80) smoothness_worst>=-1.500666 86 7 M (0.91860465 0.08139535) *
## 81) smoothness_worst< -1.500666 45 17 M (0.62222222 0.37777778) *
## 41) texture_mean< 2.912343 8 2 B (0.25000000 0.75000000)
## 82) smoothness_mean< -2.17953 2 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean>=-2.17953 6 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.030876 9 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.28279 268 107 B (0.39925373 0.60074627)
## 22) smoothness_worst< -1.509803 176 88 M (0.50000000 0.50000000)
## 44) texture_worst>=4.415916 161 73 M (0.54658385 0.45341615)
## 88) symmetry_worst>=-1.801537 69 18 M (0.73913043 0.26086957) *
## 89) symmetry_worst< -1.801537 92 37 B (0.40217391 0.59782609) *
## 45) texture_worst< 4.415916 15 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst>=-1.509803 92 19 B (0.20652174 0.79347826)
## 46) texture_worst>=4.890484 26 12 M (0.53846154 0.46153846)
## 92) smoothness_worst>=-1.48132 13 1 M (0.92307692 0.07692308) *
## 93) smoothness_worst< -1.48132 13 2 B (0.15384615 0.84615385) *
## 47) texture_worst< 4.890484 66 5 B (0.07575758 0.92424242)
## 94) smoothness_mean< -2.37669 7 3 M (0.57142857 0.42857143) *
## 95) smoothness_mean>=-2.37669 59 1 B (0.01694915 0.98305085) *
## 3) texture_mean< 2.892591 280 101 B (0.36071429 0.63928571)
## 6) compactness_se>=-4.198706 215 92 B (0.42790698 0.57209302)
## 12) compactness_se< -3.427747 173 86 M (0.50289017 0.49710983)
## 24) compactness_se>=-3.894783 93 34 M (0.63440860 0.36559140)
## 48) texture_worst>=4.250385 32 4 M (0.87500000 0.12500000)
## 96) smoothness_mean>=-2.358315 28 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.358315 4 0 B (0.00000000 1.00000000) *
## 49) texture_worst< 4.250385 61 30 M (0.50819672 0.49180328)
## 98) texture_worst< 3.895613 26 4 M (0.84615385 0.15384615) *
## 99) texture_worst>=3.895613 35 9 B (0.25714286 0.74285714) *
## 25) compactness_se< -3.894783 80 28 B (0.35000000 0.65000000)
## 50) compactness_se< -4.160164 22 3 M (0.86363636 0.13636364)
## 100) texture_mean>=2.779034 19 0 M (1.00000000 0.00000000) *
## 101) texture_mean< 2.779034 3 0 B (0.00000000 1.00000000) *
## 51) compactness_se>=-4.160164 58 9 B (0.15517241 0.84482759)
## 102) smoothness_worst>=-1.451541 11 2 M (0.81818182 0.18181818) *
## 103) smoothness_worst< -1.451541 47 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.427747 42 5 B (0.11904762 0.88095238)
## 26) smoothness_mean>=-2.069166 4 1 M (0.75000000 0.25000000)
## 52) texture_mean>=2.65428 3 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.65428 1 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.069166 38 2 B (0.05263158 0.94736842)
## 54) symmetry_worst>=-1.316602 3 1 M (0.66666667 0.33333333)
## 108) texture_mean< 2.830318 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.830318 1 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.316602 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -4.198706 65 9 B (0.13846154 0.86153846)
## 14) texture_mean>=2.87384 10 4 M (0.60000000 0.40000000)
## 28) texture_mean< 2.884497 6 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.884497 4 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 2.87384 55 3 B (0.05454545 0.94545455)
## 30) texture_worst>=4.600092 13 3 B (0.23076923 0.76923077)
## 60) smoothness_mean< -2.330887 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.330887 10 0 B (0.00000000 1.00000000) *
## 31) texture_worst< 4.600092 42 0 B (0.00000000 1.00000000) *
##
## $trees[[26]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) texture_mean>=2.960364 515 227 M (0.55922330 0.44077670)
## 4) texture_worst>=4.354728 499 211 M (0.57715431 0.42284569)
## 8) texture_worst< 4.644679 159 43 M (0.72955975 0.27044025)
## 16) smoothness_worst>=-1.498254 48 2 M (0.95833333 0.04166667)
## 32) compactness_se>=-4.35833 47 1 M (0.97872340 0.02127660)
## 64) symmetry_worst>=-1.833099 42 0 M (1.00000000 0.00000000) *
## 65) symmetry_worst< -1.833099 5 1 M (0.80000000 0.20000000) *
## 33) compactness_se< -4.35833 1 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.498254 111 41 M (0.63063063 0.36936937)
## 34) smoothness_mean< -2.229408 100 30 M (0.70000000 0.30000000)
## 68) compactness_se< -2.82386 91 21 M (0.76923077 0.23076923) *
## 69) compactness_se>=-2.82386 9 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.229408 11 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.644679 340 168 M (0.50588235 0.49411765)
## 18) texture_worst>=5.016194 100 27 M (0.73000000 0.27000000)
## 36) symmetry_worst>=-2.063111 77 9 M (0.88311688 0.11688312)
## 72) smoothness_mean>=-2.58821 76 8 M (0.89473684 0.10526316) *
## 73) smoothness_mean< -2.58821 1 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -2.063111 23 5 B (0.21739130 0.78260870)
## 74) compactness_se>=-3.413706 5 0 M (1.00000000 0.00000000) *
## 75) compactness_se< -3.413706 18 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 5.016194 240 99 B (0.41250000 0.58750000)
## 38) compactness_se< -4.436859 32 7 M (0.78125000 0.21875000)
## 76) compactness_se>=-4.590265 25 1 M (0.96000000 0.04000000) *
## 77) compactness_se< -4.590265 7 1 B (0.14285714 0.85714286) *
## 39) compactness_se>=-4.436859 208 74 B (0.35576923 0.64423077)
## 78) symmetry_worst< -2.000522 50 18 M (0.64000000 0.36000000) *
## 79) symmetry_worst>=-2.000522 158 42 B (0.26582278 0.73417722) *
## 5) texture_worst< 4.354728 16 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.960364 397 148 B (0.37279597 0.62720403)
## 6) texture_mean< 2.948902 363 148 B (0.40771350 0.59228650)
## 12) texture_mean>=2.927988 56 17 M (0.69642857 0.30357143)
## 24) compactness_se>=-4.177518 51 12 M (0.76470588 0.23529412)
## 48) texture_mean< 2.938103 23 0 M (1.00000000 0.00000000) *
## 49) texture_mean>=2.938103 28 12 M (0.57142857 0.42857143)
## 98) texture_mean>=2.947329 12 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.947329 16 4 B (0.25000000 0.75000000) *
## 25) compactness_se< -4.177518 5 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.927988 307 109 B (0.35504886 0.64495114)
## 26) smoothness_worst< -1.539792 95 47 M (0.50526316 0.49473684)
## 52) smoothness_worst>=-1.547262 18 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.547262 77 30 B (0.38961039 0.61038961)
## 106) smoothness_mean< -2.436819 41 18 M (0.56097561 0.43902439) *
## 107) smoothness_mean>=-2.436819 36 7 B (0.19444444 0.80555556) *
## 27) smoothness_worst>=-1.539792 212 61 B (0.28773585 0.71226415)
## 54) compactness_se>=-4.198706 166 61 B (0.36746988 0.63253012)
## 108) texture_mean< 2.893423 137 60 B (0.43795620 0.56204380) *
## 109) texture_mean>=2.893423 29 1 B (0.03448276 0.96551724) *
## 55) compactness_se< -4.198706 46 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=2.948902 34 0 B (0.00000000 1.00000000) *
##
## $trees[[27]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 422 M (0.53728070 0.46271930)
## 2) texture_mean>=2.709047 873 389 M (0.55441008 0.44558992)
## 4) compactness_se>=-3.721197 350 125 M (0.64285714 0.35714286)
## 8) compactness_se< -3.57681 68 6 M (0.91176471 0.08823529)
## 16) smoothness_mean>=-2.509667 66 4 M (0.93939394 0.06060606)
## 32) texture_worst< 4.855419 54 0 M (1.00000000 0.00000000) *
## 33) texture_worst>=4.855419 12 4 M (0.66666667 0.33333333)
## 66) texture_mean>=3.340739 8 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.340739 4 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.509667 2 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-3.57681 282 119 M (0.57801418 0.42198582)
## 18) smoothness_worst>=-1.618016 251 94 M (0.62549801 0.37450199)
## 36) texture_mean>=3.058688 84 16 M (0.80952381 0.19047619)
## 72) smoothness_worst< -1.468038 68 5 M (0.92647059 0.07352941) *
## 73) smoothness_worst>=-1.468038 16 5 B (0.31250000 0.68750000) *
## 37) texture_mean< 3.058688 167 78 M (0.53293413 0.46706587)
## 74) texture_mean< 3.001714 111 32 M (0.71171171 0.28828829) *
## 75) texture_mean>=3.001714 56 10 B (0.17857143 0.82142857) *
## 19) smoothness_worst< -1.618016 31 6 B (0.19354839 0.80645161)
## 38) smoothness_worst< -1.694287 5 0 M (1.00000000 0.00000000) *
## 39) smoothness_worst>=-1.694287 26 1 B (0.03846154 0.96153846)
## 78) texture_mean>=3.166164 1 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.166164 25 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.721197 523 259 B (0.49521989 0.50478011)
## 10) smoothness_worst>=-1.48191 161 57 M (0.64596273 0.35403727)
## 20) compactness_se>=-4.510489 148 44 M (0.70270270 0.29729730)
## 40) smoothness_mean< -2.235394 122 27 M (0.77868852 0.22131148)
## 80) texture_worst< 4.550742 66 4 M (0.93939394 0.06060606) *
## 81) texture_worst>=4.550742 56 23 M (0.58928571 0.41071429) *
## 41) smoothness_mean>=-2.235394 26 9 B (0.34615385 0.65384615)
## 82) texture_mean>=2.939917 12 3 M (0.75000000 0.25000000) *
## 83) texture_mean< 2.939917 14 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.510489 13 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.48191 362 155 B (0.42817680 0.57182320)
## 22) compactness_se< -3.869459 313 155 B (0.49520767 0.50479233)
## 44) smoothness_mean< -2.294121 287 132 M (0.54006969 0.45993031)
## 88) texture_mean>=2.892314 224 83 M (0.62946429 0.37053571) *
## 89) texture_mean< 2.892314 63 14 B (0.22222222 0.77777778) *
## 45) smoothness_mean>=-2.294121 26 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-3.869459 49 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.709047 39 6 B (0.15384615 0.84615385)
## 6) texture_worst< 3.858337 14 6 B (0.42857143 0.57142857)
## 12) compactness_se>=-3.808227 8 2 M (0.75000000 0.25000000)
## 24) smoothness_mean< -1.942706 6 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean>=-1.942706 2 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -3.808227 6 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=3.858337 25 0 B (0.00000000 1.00000000) *
##
## $trees[[28]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 445 B (0.48793860 0.51206140)
## 2) texture_mean>=3.058002 289 116 M (0.59861592 0.40138408)
## 4) smoothness_worst>=-1.603555 234 75 M (0.67948718 0.32051282)
## 8) texture_worst< 4.797934 78 5 M (0.93589744 0.06410256)
## 16) texture_worst>=4.496164 74 1 M (0.98648649 0.01351351)
## 32) smoothness_worst< -1.469988 73 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst>=-1.469988 1 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.496164 4 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.797934 156 70 M (0.55128205 0.44871795)
## 18) smoothness_mean>=-2.501158 141 55 M (0.60992908 0.39007092)
## 36) texture_worst>=4.897936 116 36 M (0.68965517 0.31034483)
## 72) texture_mean>=3.082368 108 29 M (0.73148148 0.26851852) *
## 73) texture_mean< 3.082368 8 1 B (0.12500000 0.87500000) *
## 37) texture_worst< 4.897936 25 6 B (0.24000000 0.76000000)
## 74) texture_mean< 3.09883 6 1 M (0.83333333 0.16666667) *
## 75) texture_mean>=3.09883 19 1 B (0.05263158 0.94736842) *
## 19) smoothness_mean< -2.501158 15 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.603555 55 14 B (0.25454545 0.74545455)
## 10) compactness_se>=-3.013033 9 1 M (0.88888889 0.11111111)
## 20) texture_mean>=3.076827 8 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.076827 1 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.013033 46 6 B (0.13043478 0.86956522)
## 22) compactness_se< -4.507137 10 4 M (0.60000000 0.40000000)
## 44) compactness_se>=-4.572499 6 0 M (1.00000000 0.00000000) *
## 45) compactness_se< -4.572499 4 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-4.507137 36 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.058002 623 272 B (0.43659711 0.56340289)
## 6) symmetry_worst>=-1.325507 30 4 M (0.86666667 0.13333333)
## 12) compactness_se< -2.588521 27 2 M (0.92592593 0.07407407)
## 24) smoothness_mean< -2.022167 26 1 M (0.96153846 0.03846154)
## 48) smoothness_mean>=-2.340816 24 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean< -2.340816 2 1 M (0.50000000 0.50000000)
## 98) texture_mean>=2.868073 1 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.868073 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.022167 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-2.588521 3 1 B (0.33333333 0.66666667)
## 26) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 2.915767 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.325507 593 246 B (0.41483980 0.58516020)
## 14) compactness_se< -3.476676 462 212 B (0.45887446 0.54112554)
## 28) compactness_se>=-3.883198 167 63 M (0.62275449 0.37724551)
## 56) smoothness_mean>=-2.321775 89 20 M (0.77528090 0.22471910)
## 112) smoothness_worst>=-1.456304 30 1 M (0.96666667 0.03333333) *
## 113) smoothness_worst< -1.456304 59 19 M (0.67796610 0.32203390) *
## 57) smoothness_mean< -2.321775 78 35 B (0.44871795 0.55128205)
## 114) smoothness_worst< -1.598495 23 2 M (0.91304348 0.08695652) *
## 115) smoothness_worst>=-1.598495 55 14 B (0.25454545 0.74545455) *
## 29) compactness_se< -3.883198 295 108 B (0.36610169 0.63389831)
## 58) texture_mean>=2.809391 261 108 B (0.41379310 0.58620690)
## 116) texture_mean< 2.848102 34 9 M (0.73529412 0.26470588) *
## 117) texture_mean>=2.848102 227 83 B (0.36563877 0.63436123) *
## 59) texture_mean< 2.809391 34 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-3.476676 131 34 B (0.25954198 0.74045802)
## 30) smoothness_worst>=-1.502084 56 27 B (0.48214286 0.51785714)
## 60) smoothness_worst< -1.468619 25 4 M (0.84000000 0.16000000)
## 120) texture_mean>=2.8622 18 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.8622 7 3 B (0.42857143 0.57142857) *
## 61) smoothness_worst>=-1.468619 31 6 B (0.19354839 0.80645161)
## 122) smoothness_mean>=-2.049356 3 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.049356 28 3 B (0.10714286 0.89285714) *
## 31) smoothness_worst< -1.502084 75 7 B (0.09333333 0.90666667)
## 62) texture_mean>=3.038537 2 0 M (1.00000000 0.00000000) *
## 63) texture_mean< 3.038537 73 5 B (0.06849315 0.93150685)
## 126) smoothness_worst< -1.568787 19 5 B (0.26315789 0.73684211) *
## 127) smoothness_worst>=-1.568787 54 0 B (0.00000000 1.00000000) *
##
## $trees[[29]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 B (0.47697368 0.52302632)
## 2) texture_worst>=4.362076 703 335 M (0.52347084 0.47652916)
## 4) texture_mean< 3.087399 504 212 M (0.57936508 0.42063492)
## 8) smoothness_mean>=-2.199595 40 5 M (0.87500000 0.12500000)
## 16) texture_mean>=2.918531 29 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.918531 11 5 M (0.54545455 0.45454545)
## 34) texture_mean< 2.899496 6 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=2.899496 5 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.199595 464 207 M (0.55387931 0.44612069)
## 18) smoothness_mean< -2.235862 408 165 M (0.59558824 0.40441176)
## 36) smoothness_mean>=-2.391854 230 71 M (0.69130435 0.30869565)
## 72) smoothness_worst< -1.476997 139 23 M (0.83453237 0.16546763) *
## 73) smoothness_worst>=-1.476997 91 43 B (0.47252747 0.52747253) *
## 37) smoothness_mean< -2.391854 178 84 B (0.47191011 0.52808989)
## 74) smoothness_worst< -1.549836 137 56 M (0.59124088 0.40875912) *
## 75) smoothness_worst>=-1.549836 41 3 B (0.07317073 0.92682927) *
## 19) smoothness_mean>=-2.235862 56 14 B (0.25000000 0.75000000)
## 38) compactness_se< -4.140724 12 2 M (0.83333333 0.16666667)
## 76) smoothness_mean>=-2.21595 10 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean< -2.21595 2 0 B (0.00000000 1.00000000) *
## 39) compactness_se>=-4.140724 44 4 B (0.09090909 0.90909091)
## 78) texture_mean>=3.04949 4 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.04949 40 0 B (0.00000000 1.00000000) *
## 5) texture_mean>=3.087399 199 76 B (0.38190955 0.61809045)
## 10) smoothness_worst>=-1.603555 162 73 B (0.45061728 0.54938272)
## 20) texture_worst< 4.803681 31 6 M (0.80645161 0.19354839)
## 40) smoothness_mean< -2.29363 24 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.29363 7 1 B (0.14285714 0.85714286)
## 82) smoothness_mean>=-2.242961 1 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.242961 6 0 B (0.00000000 1.00000000) *
## 21) texture_worst>=4.803681 131 48 B (0.36641221 0.63358779)
## 42) smoothness_worst< -1.582589 7 0 M (1.00000000 0.00000000) *
## 43) smoothness_worst>=-1.582589 124 41 B (0.33064516 0.66935484)
## 86) texture_mean< 3.171358 31 14 M (0.54838710 0.45161290) *
## 87) texture_mean>=3.171358 93 24 B (0.25806452 0.74193548) *
## 11) smoothness_worst< -1.603555 37 3 B (0.08108108 0.91891892)
## 22) smoothness_mean>=-2.337942 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.337942 35 1 B (0.02857143 0.97142857)
## 46) symmetry_worst< -2.632248 1 0 M (1.00000000 0.00000000) *
## 47) symmetry_worst>=-2.632248 34 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.362076 209 67 B (0.32057416 0.67942584)
## 6) compactness_se>=-4.173143 175 67 B (0.38285714 0.61714286)
## 12) compactness_se< -4.160164 8 0 M (1.00000000 0.00000000) *
## 13) compactness_se>=-4.160164 167 59 B (0.35329341 0.64670659)
## 26) texture_mean< 2.801532 105 49 B (0.46666667 0.53333333)
## 52) texture_worst>=4.178472 27 4 M (0.85185185 0.14814815)
## 104) compactness_se>=-3.892047 23 0 M (1.00000000 0.00000000) *
## 105) compactness_se< -3.892047 4 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.178472 78 26 B (0.33333333 0.66666667)
## 106) compactness_se>=-2.94014 7 1 M (0.85714286 0.14285714) *
## 107) compactness_se< -2.94014 71 20 B (0.28169014 0.71830986) *
## 27) texture_mean>=2.801532 62 10 B (0.16129032 0.83870968)
## 54) texture_worst< 4.138009 4 0 M (1.00000000 0.00000000) *
## 55) texture_worst>=4.138009 58 6 B (0.10344828 0.89655172)
## 110) smoothness_worst>=-1.384694 2 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst< -1.384694 56 4 B (0.07142857 0.92857143) *
## 7) compactness_se< -4.173143 34 0 B (0.00000000 1.00000000) *
##
## $trees[[30]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 B (0.48245614 0.51754386)
## 2) smoothness_mean>=-2.424301 673 308 M (0.54234770 0.45765230)
## 4) symmetry_worst< -2.379234 29 2 M (0.93103448 0.06896552)
## 8) texture_mean>=2.855865 28 1 M (0.96428571 0.03571429)
## 16) smoothness_mean< -2.287736 25 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean>=-2.287736 3 1 M (0.66666667 0.33333333)
## 34) texture_mean>=3.050671 2 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.050671 1 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.855865 1 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-2.379234 644 306 M (0.52484472 0.47515528)
## 10) smoothness_worst>=-1.559144 554 240 M (0.56678700 0.43321300)
## 20) smoothness_worst< -1.536824 76 10 M (0.86842105 0.13157895)
## 40) symmetry_worst< -1.583647 71 6 M (0.91549296 0.08450704)
## 80) texture_mean>=2.6809 69 4 M (0.94202899 0.05797101) *
## 81) texture_mean< 2.6809 2 0 B (0.00000000 1.00000000) *
## 41) symmetry_worst>=-1.583647 5 1 B (0.20000000 0.80000000)
## 82) texture_mean>=3.07122 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.07122 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst>=-1.536824 478 230 M (0.51882845 0.48117155)
## 42) smoothness_worst>=-1.525694 440 198 M (0.55000000 0.45000000)
## 84) compactness_se>=-4.557422 427 185 M (0.56674473 0.43325527) *
## 85) compactness_se< -4.557422 13 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst< -1.525694 38 6 B (0.15789474 0.84210526)
## 86) smoothness_mean>=-2.170258 6 1 M (0.83333333 0.16666667) *
## 87) smoothness_mean< -2.170258 32 1 B (0.03125000 0.96875000) *
## 11) smoothness_worst< -1.559144 90 24 B (0.26666667 0.73333333)
## 22) compactness_se>=-3.745127 46 23 M (0.50000000 0.50000000)
## 44) texture_worst>=4.585652 22 4 M (0.81818182 0.18181818)
## 88) smoothness_worst>=-1.618016 19 1 M (0.94736842 0.05263158) *
## 89) smoothness_worst< -1.618016 3 0 B (0.00000000 1.00000000) *
## 45) texture_worst< 4.585652 24 5 B (0.20833333 0.79166667)
## 90) texture_mean>=2.969227 3 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.969227 21 2 B (0.09523810 0.90476190) *
## 23) compactness_se< -3.745127 44 1 B (0.02272727 0.97727273)
## 46) smoothness_mean< -2.399947 3 1 B (0.33333333 0.66666667)
## 92) texture_mean< 3.119032 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean>=3.119032 2 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean>=-2.399947 41 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.424301 239 75 B (0.31380753 0.68619247)
## 6) smoothness_worst< -1.551775 182 70 B (0.38461538 0.61538462)
## 12) smoothness_worst>=-1.556752 26 3 M (0.88461538 0.11538462)
## 24) texture_mean>=2.850634 23 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.850634 3 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.556752 156 47 B (0.30128205 0.69871795)
## 26) smoothness_worst< -1.576547 123 47 B (0.38211382 0.61788618)
## 52) symmetry_worst>=-1.781697 42 13 M (0.69047619 0.30952381)
## 104) smoothness_mean< -2.47008 34 6 M (0.82352941 0.17647059) *
## 105) smoothness_mean>=-2.47008 8 1 B (0.12500000 0.87500000) *
## 53) symmetry_worst< -1.781697 81 18 B (0.22222222 0.77777778)
## 106) compactness_se>=-3.514597 23 9 M (0.60869565 0.39130435) *
## 107) compactness_se< -3.514597 58 4 B (0.06896552 0.93103448) *
## 27) smoothness_worst>=-1.576547 33 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.551775 57 5 B (0.08771930 0.91228070)
## 14) symmetry_worst< -1.893206 5 0 M (1.00000000 0.00000000) *
## 15) symmetry_worst>=-1.893206 52 0 B (0.00000000 1.00000000) *
##
## $trees[[31]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 381 B (0.41776316 0.58223684)
## 2) symmetry_worst>=-1.366937 34 8 M (0.76470588 0.23529412)
## 4) compactness_se>=-3.486288 17 0 M (1.00000000 0.00000000) *
## 5) compactness_se< -3.486288 17 8 M (0.52941176 0.47058824)
## 10) compactness_se< -4.00428 7 0 M (1.00000000 0.00000000) *
## 11) compactness_se>=-4.00428 10 2 B (0.20000000 0.80000000)
## 22) smoothness_mean< -2.419235 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.419235 8 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 878 355 B (0.40432802 0.59567198)
## 6) symmetry_worst< -1.56292 731 318 B (0.43502052 0.56497948)
## 12) smoothness_worst>=-1.637109 684 312 B (0.45614035 0.54385965)
## 24) smoothness_mean< -2.299091 435 209 M (0.51954023 0.48045977)
## 48) texture_worst< 4.465917 120 32 M (0.73333333 0.26666667)
## 96) compactness_se< -3.377574 110 22 M (0.80000000 0.20000000) *
## 97) compactness_se>=-3.377574 10 0 B (0.00000000 1.00000000) *
## 49) texture_worst>=4.465917 315 138 B (0.43809524 0.56190476)
## 98) texture_worst>=4.578048 240 120 M (0.50000000 0.50000000) *
## 99) texture_worst< 4.578048 75 18 B (0.24000000 0.76000000) *
## 25) smoothness_mean>=-2.299091 249 86 B (0.34538153 0.65461847)
## 50) compactness_se>=-3.02233 14 1 M (0.92857143 0.07142857)
## 100) texture_mean>=2.81216 13 0 M (1.00000000 0.00000000) *
## 101) texture_mean< 2.81216 1 0 B (0.00000000 1.00000000) *
## 51) compactness_se< -3.02233 235 73 B (0.31063830 0.68936170)
## 102) symmetry_worst>=-1.65118 37 12 M (0.67567568 0.32432432) *
## 103) symmetry_worst< -1.65118 198 48 B (0.24242424 0.75757576) *
## 13) smoothness_worst< -1.637109 47 6 B (0.12765957 0.87234043)
## 26) compactness_se>=-2.979429 7 3 M (0.57142857 0.42857143)
## 52) texture_mean>=3.076827 4 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 3.076827 3 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -2.979429 40 2 B (0.05000000 0.95000000)
## 54) smoothness_mean>=-2.38784 1 0 M (1.00000000 0.00000000) *
## 55) smoothness_mean< -2.38784 39 1 B (0.02564103 0.97435897)
## 110) symmetry_worst>=-1.800994 4 1 B (0.25000000 0.75000000) *
## 111) symmetry_worst< -1.800994 35 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.56292 147 37 B (0.25170068 0.74829932)
## 14) smoothness_mean>=-2.155028 8 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.155028 139 29 B (0.20863309 0.79136691)
## 30) texture_worst>=5.204837 6 0 M (1.00000000 0.00000000) *
## 31) texture_worst< 5.204837 133 23 B (0.17293233 0.82706767)
## 62) smoothness_mean< -2.454281 15 7 B (0.46666667 0.53333333)
## 124) smoothness_mean>=-2.462871 7 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.462871 8 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.454281 118 16 B (0.13559322 0.86440678)
## 126) texture_mean>=3.01402 31 10 B (0.32258065 0.67741935) *
## 127) texture_mean< 3.01402 87 6 B (0.06896552 0.93103448) *
##
## $trees[[32]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 385 B (0.42214912 0.57785088)
## 2) symmetry_worst< -2.49184 28 5 M (0.82142857 0.17857143)
## 4) smoothness_worst< -1.534654 25 2 M (0.92000000 0.08000000)
## 8) texture_mean>=2.861235 23 0 M (1.00000000 0.00000000) *
## 9) texture_mean< 2.861235 2 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.534654 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst>=-2.49184 884 362 B (0.40950226 0.59049774)
## 6) smoothness_worst>=-1.558926 661 302 B (0.45688351 0.54311649)
## 12) smoothness_mean>=-2.48495 631 302 B (0.47860539 0.52139461)
## 24) smoothness_worst< -1.541278 87 21 M (0.75862069 0.24137931)
## 48) smoothness_mean< -2.313857 81 15 M (0.81481481 0.18518519)
## 96) symmetry_worst< -1.809006 55 1 M (0.98181818 0.01818182) *
## 97) symmetry_worst>=-1.809006 26 12 B (0.46153846 0.53846154) *
## 49) smoothness_mean>=-2.313857 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.541278 544 236 B (0.43382353 0.56617647)
## 50) symmetry_worst>=-2.178473 512 235 B (0.45898438 0.54101562)
## 100) texture_mean>=2.836149 404 196 M (0.51485149 0.48514851) *
## 101) texture_mean< 2.836149 108 27 B (0.25000000 0.75000000) *
## 51) symmetry_worst< -2.178473 32 1 B (0.03125000 0.96875000)
## 102) texture_worst>=4.947241 6 1 B (0.16666667 0.83333333) *
## 103) texture_worst< 4.947241 26 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.48495 30 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.558926 223 60 B (0.26905830 0.73094170)
## 14) smoothness_worst< -1.57166 165 56 B (0.33939394 0.66060606)
## 28) symmetry_worst>=-1.787851 56 25 M (0.55357143 0.44642857)
## 56) texture_mean>=2.933058 47 16 M (0.65957447 0.34042553)
## 112) texture_worst< 4.733599 30 5 M (0.83333333 0.16666667) *
## 113) texture_worst>=4.733599 17 6 B (0.35294118 0.64705882) *
## 57) texture_mean< 2.933058 9 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.787851 109 25 B (0.22935780 0.77064220)
## 58) texture_worst>=4.898911 22 10 M (0.54545455 0.45454545)
## 116) texture_worst< 5.13268 10 0 M (1.00000000 0.00000000) *
## 117) texture_worst>=5.13268 12 2 B (0.16666667 0.83333333) *
## 59) texture_worst< 4.898911 87 13 B (0.14942529 0.85057471)
## 118) smoothness_worst< -1.709736 6 1 M (0.83333333 0.16666667) *
## 119) smoothness_worst>=-1.709736 81 8 B (0.09876543 0.90123457) *
## 15) smoothness_worst>=-1.57166 58 4 B (0.06896552 0.93103448)
## 30) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 31) compactness_se< -2.682598 56 2 B (0.03571429 0.96428571)
## 62) smoothness_mean>=-2.299648 1 0 M (1.00000000 0.00000000) *
## 63) smoothness_mean< -2.299648 55 1 B (0.01818182 0.98181818)
## 126) smoothness_worst< -1.568787 7 1 B (0.14285714 0.85714286) *
## 127) smoothness_worst>=-1.568787 48 0 B (0.00000000 1.00000000) *
##
## $trees[[33]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 382 B (0.41885965 0.58114035)
## 2) symmetry_worst>=-1.353976 49 13 M (0.73469388 0.26530612)
## 4) symmetry_worst< -1.244631 26 2 M (0.92307692 0.07692308)
## 8) compactness_se>=-3.807179 23 0 M (1.00000000 0.00000000) *
## 9) compactness_se< -3.807179 3 1 B (0.33333333 0.66666667)
## 18) texture_mean>=2.933998 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 2.933998 2 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst>=-1.244631 23 11 M (0.52173913 0.47826087)
## 10) symmetry_worst>=-1.072749 12 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.072749 11 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.353976 863 346 B (0.40092700 0.59907300)
## 6) smoothness_mean>=-2.425205 656 291 B (0.44359756 0.55640244)
## 12) smoothness_mean< -2.233059 531 260 B (0.48964218 0.51035782)
## 24) texture_worst< 4.555292 249 97 M (0.61044177 0.38955823)
## 48) symmetry_worst< -1.700875 185 55 M (0.70270270 0.29729730)
## 96) compactness_se< -3.48728 138 28 M (0.79710145 0.20289855) *
## 97) compactness_se>=-3.48728 47 20 B (0.42553191 0.57446809) *
## 49) symmetry_worst>=-1.700875 64 22 B (0.34375000 0.65625000)
## 98) texture_worst>=4.517878 9 0 M (1.00000000 0.00000000) *
## 99) texture_worst< 4.517878 55 13 B (0.23636364 0.76363636) *
## 25) texture_worst>=4.555292 282 108 B (0.38297872 0.61702128)
## 50) smoothness_worst>=-1.419909 15 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst< -1.419909 267 93 B (0.34831461 0.65168539)
## 102) smoothness_worst< -1.484675 146 67 B (0.45890411 0.54109589) *
## 103) smoothness_worst>=-1.484675 121 26 B (0.21487603 0.78512397) *
## 13) smoothness_mean>=-2.233059 125 31 B (0.24800000 0.75200000)
## 26) texture_worst>=5.026995 6 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 5.026995 119 25 B (0.21008403 0.78991597)
## 54) smoothness_worst< -1.531558 6 0 M (1.00000000 0.00000000) *
## 55) smoothness_worst>=-1.531558 113 19 B (0.16814159 0.83185841)
## 110) symmetry_worst>=-1.532237 17 8 M (0.52941176 0.47058824) *
## 111) symmetry_worst< -1.532237 96 10 B (0.10416667 0.89583333) *
## 7) smoothness_mean< -2.425205 207 55 B (0.26570048 0.73429952)
## 14) smoothness_worst>=-1.653746 172 55 B (0.31976744 0.68023256)
## 28) smoothness_worst< -1.576547 79 39 B (0.49367089 0.50632911)
## 56) symmetry_worst>=-2.050548 64 25 M (0.60937500 0.39062500)
## 112) compactness_se< -3.427985 56 17 M (0.69642857 0.30357143) *
## 113) compactness_se>=-3.427985 8 0 B (0.00000000 1.00000000) *
## 57) symmetry_worst< -2.050548 15 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst>=-1.576547 93 16 B (0.17204301 0.82795699)
## 58) texture_mean>=3.431166 3 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.431166 90 13 B (0.14444444 0.85555556)
## 118) texture_worst< 4.62656 40 12 B (0.30000000 0.70000000) *
## 119) texture_worst>=4.62656 50 1 B (0.02000000 0.98000000) *
## 15) smoothness_worst< -1.653746 35 0 B (0.00000000 1.00000000) *
##
## $trees[[34]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 415 B (0.45504386 0.54495614)
## 2) smoothness_worst< -1.54469 286 128 M (0.55244755 0.44755245)
## 4) smoothness_worst>=-1.55958 84 16 M (0.80952381 0.19047619)
## 8) compactness_se>=-4.694501 80 12 M (0.85000000 0.15000000)
## 16) texture_mean< 3.353705 78 10 M (0.87179487 0.12820513)
## 32) smoothness_mean< -2.313857 70 6 M (0.91428571 0.08571429)
## 64) symmetry_worst< -1.801798 55 1 M (0.98181818 0.01818182) *
## 65) symmetry_worst>=-1.801798 15 5 M (0.66666667 0.33333333) *
## 33) smoothness_mean>=-2.313857 8 4 M (0.50000000 0.50000000)
## 66) texture_mean< 3.271203 4 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.271203 4 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.353705 2 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.694501 4 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.55958 202 90 B (0.44554455 0.55445545)
## 10) smoothness_worst< -1.568787 170 83 M (0.51176471 0.48823529)
## 20) smoothness_mean>=-2.337942 25 2 M (0.92000000 0.08000000)
## 40) smoothness_mean< -2.294641 18 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.294641 7 2 M (0.71428571 0.28571429)
## 82) smoothness_mean>=-2.214186 5 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.214186 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.337942 145 64 B (0.44137931 0.55862069)
## 42) symmetry_worst>=-1.795801 43 12 M (0.72093023 0.27906977)
## 84) texture_mean>=2.939162 33 5 M (0.84848485 0.15151515) *
## 85) texture_mean< 2.939162 10 3 B (0.30000000 0.70000000) *
## 43) symmetry_worst< -1.795801 102 33 B (0.32352941 0.67647059)
## 86) smoothness_worst< -1.694089 7 0 M (1.00000000 0.00000000) *
## 87) smoothness_worst>=-1.694089 95 26 B (0.27368421 0.72631579) *
## 11) smoothness_worst>=-1.568787 32 3 B (0.09375000 0.90625000)
## 22) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 23) compactness_se< -2.682598 30 1 B (0.03333333 0.96666667)
## 46) smoothness_mean>=-2.296106 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.296106 29 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.54469 626 257 B (0.41054313 0.58945687)
## 6) smoothness_worst>=-1.501069 464 215 B (0.46336207 0.53663793)
## 12) texture_mean< 3.039744 358 173 M (0.51675978 0.48324022)
## 24) texture_mean>=2.967331 101 20 M (0.80198020 0.19801980)
## 48) symmetry_worst>=-1.839419 74 6 M (0.91891892 0.08108108)
## 96) symmetry_worst< -1.471051 68 2 M (0.97058824 0.02941176) *
## 97) symmetry_worst>=-1.471051 6 2 B (0.33333333 0.66666667) *
## 49) symmetry_worst< -1.839419 27 13 B (0.48148148 0.51851852)
## 98) symmetry_worst< -1.878579 13 0 M (1.00000000 0.00000000) *
## 99) symmetry_worst>=-1.878579 14 0 B (0.00000000 1.00000000) *
## 25) texture_mean< 2.967331 257 104 B (0.40466926 0.59533074)
## 50) smoothness_mean< -2.267218 116 46 M (0.60344828 0.39655172)
## 100) texture_worst< 4.543572 76 17 M (0.77631579 0.22368421) *
## 101) texture_worst>=4.543572 40 11 B (0.27500000 0.72500000) *
## 51) smoothness_mean>=-2.267218 141 34 B (0.24113475 0.75886525)
## 102) symmetry_worst>=-1.74232 74 33 B (0.44594595 0.55405405) *
## 103) symmetry_worst< -1.74232 67 1 B (0.01492537 0.98507463) *
## 13) texture_mean>=3.039744 106 30 B (0.28301887 0.71698113)
## 26) compactness_se>=-3.334337 16 2 M (0.87500000 0.12500000)
## 52) smoothness_mean>=-2.420336 14 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.420336 2 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.334337 90 16 B (0.17777778 0.82222222)
## 54) symmetry_worst< -1.822663 17 8 M (0.52941176 0.47058824)
## 108) compactness_se< -3.615775 8 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-3.615775 9 1 B (0.11111111 0.88888889) *
## 55) symmetry_worst>=-1.822663 73 7 B (0.09589041 0.90410959)
## 110) smoothness_worst< -1.483884 4 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.483884 69 3 B (0.04347826 0.95652174) *
## 7) smoothness_worst< -1.501069 162 42 B (0.25925926 0.74074074)
## 14) texture_worst>=4.536474 107 39 B (0.36448598 0.63551402)
## 28) smoothness_worst< -1.510008 79 38 B (0.48101266 0.51898734)
## 56) texture_worst< 4.774294 41 13 M (0.68292683 0.31707317)
## 112) smoothness_mean>=-2.405234 34 6 M (0.82352941 0.17647059) *
## 113) smoothness_mean< -2.405234 7 0 B (0.00000000 1.00000000) *
## 57) texture_worst>=4.774294 38 10 B (0.26315789 0.73684211)
## 114) smoothness_worst>=-1.52112 7 0 M (1.00000000 0.00000000) *
## 115) smoothness_worst< -1.52112 31 3 B (0.09677419 0.90322581) *
## 29) smoothness_worst>=-1.510008 28 1 B (0.03571429 0.96428571)
## 58) compactness_se>=-3.44344 3 1 B (0.33333333 0.66666667)
## 116) texture_mean< 3.145585 1 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.145585 2 0 B (0.00000000 1.00000000) *
## 59) compactness_se< -3.44344 25 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.536474 55 3 B (0.05454545 0.94545455)
## 30) smoothness_mean>=-2.194169 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=2.816952 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.816952 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.194169 52 1 B (0.01923077 0.98076923)
## 62) smoothness_worst< -1.541278 2 1 M (0.50000000 0.50000000)
## 124) texture_mean>=2.773152 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.773152 1 0 B (0.00000000 1.00000000) *
## 63) smoothness_worst>=-1.541278 50 0 B (0.00000000 1.00000000) *
##
## $trees[[35]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 408 B (0.44736842 0.55263158)
## 2) compactness_se>=-3.721197 402 177 M (0.55970149 0.44029851)
## 4) compactness_se< -3.575734 76 10 M (0.86842105 0.13157895)
## 8) texture_mean>=2.647471 73 7 M (0.90410959 0.09589041)
## 16) texture_mean< 3.07431 54 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.07431 19 7 M (0.63157895 0.36842105)
## 34) symmetry_worst>=-1.999513 14 2 M (0.85714286 0.14285714)
## 68) texture_mean>=3.087384 12 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 3.087384 2 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst< -1.999513 5 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.647471 3 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-3.575734 326 159 B (0.48773006 0.51226994)
## 10) smoothness_worst>=-1.618016 298 141 M (0.52684564 0.47315436)
## 20) texture_worst>=5.016194 18 0 M (1.00000000 0.00000000) *
## 21) texture_worst< 5.016194 280 139 B (0.49642857 0.50357143)
## 42) smoothness_worst< -1.595509 16 1 M (0.93750000 0.06250000)
## 84) compactness_se< -3.215213 14 0 M (1.00000000 0.00000000) *
## 85) compactness_se>=-3.215213 2 1 M (0.50000000 0.50000000) *
## 43) smoothness_worst>=-1.595509 264 124 B (0.46969697 0.53030303)
## 86) smoothness_mean>=-2.294122 142 60 M (0.57746479 0.42253521) *
## 87) smoothness_mean< -2.294122 122 42 B (0.34426230 0.65573770) *
## 11) smoothness_worst< -1.618016 28 2 B (0.07142857 0.92857143)
## 22) smoothness_worst< -1.707409 3 1 M (0.66666667 0.33333333)
## 44) texture_mean< 3.103494 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst>=-1.707409 25 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 510 183 B (0.35882353 0.64117647)
## 6) texture_worst>=4.507583 346 148 B (0.42774566 0.57225434)
## 12) smoothness_mean>=-2.407891 244 119 M (0.51229508 0.48770492)
## 24) smoothness_mean< -2.382983 34 4 M (0.88235294 0.11764706)
## 48) texture_mean>=2.920077 32 2 M (0.93750000 0.06250000)
## 96) symmetry_worst>=-2.212871 30 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.920077 2 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.382983 210 95 B (0.45238095 0.54761905)
## 50) texture_mean< 2.903338 56 15 M (0.73214286 0.26785714)
## 100) texture_worst< 4.637071 43 5 M (0.88372093 0.11627907) *
## 101) texture_worst>=4.637071 13 3 B (0.23076923 0.76923077) *
## 51) texture_mean>=2.903338 154 54 B (0.35064935 0.64935065)
## 102) smoothness_worst>=-1.472892 70 30 M (0.57142857 0.42857143) *
## 103) smoothness_worst< -1.472892 84 14 B (0.16666667 0.83333333) *
## 13) smoothness_mean< -2.407891 102 23 B (0.22549020 0.77450980)
## 26) texture_worst>=4.853342 44 17 B (0.38636364 0.61363636)
## 52) texture_worst< 4.985267 19 6 M (0.68421053 0.31578947)
## 104) texture_mean< 3.162414 13 0 M (1.00000000 0.00000000) *
## 105) texture_mean>=3.162414 6 0 B (0.00000000 1.00000000) *
## 53) texture_worst>=4.985267 25 4 B (0.16000000 0.84000000)
## 106) smoothness_mean>=-2.427246 3 1 M (0.66666667 0.33333333) *
## 107) smoothness_mean< -2.427246 22 2 B (0.09090909 0.90909091) *
## 27) texture_worst< 4.853342 58 6 B (0.10344828 0.89655172)
## 54) symmetry_worst>=-1.541105 17 6 B (0.35294118 0.64705882)
## 108) smoothness_mean< -2.449189 4 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean>=-2.449189 13 2 B (0.15384615 0.84615385) *
## 55) symmetry_worst< -1.541105 41 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.507583 164 35 B (0.21341463 0.78658537)
## 14) smoothness_mean< -2.411844 42 18 B (0.42857143 0.57142857)
## 28) symmetry_worst>=-1.963801 28 10 M (0.64285714 0.35714286)
## 56) texture_worst< 4.465917 22 4 M (0.81818182 0.18181818)
## 112) compactness_se>=-4.531581 19 1 M (0.94736842 0.05263158) *
## 113) compactness_se< -4.531581 3 0 B (0.00000000 1.00000000) *
## 57) texture_worst>=4.465917 6 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.963801 14 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.411844 122 17 B (0.13934426 0.86065574)
## 30) symmetry_worst< -2.391709 5 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst>=-2.391709 117 12 B (0.10256410 0.89743590)
## 62) compactness_se>=-3.892047 22 10 B (0.45454545 0.54545455)
## 124) smoothness_worst>=-1.482701 11 1 M (0.90909091 0.09090909) *
## 125) smoothness_worst< -1.482701 11 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.892047 95 2 B (0.02105263 0.97894737)
## 126) smoothness_worst>=-1.42613 3 1 M (0.66666667 0.33333333) *
## 127) smoothness_worst< -1.42613 92 0 B (0.00000000 1.00000000) *
##
## $trees[[36]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 398 B (0.43640351 0.56359649)
## 2) symmetry_worst>=-1.424186 75 22 M (0.70666667 0.29333333)
## 4) texture_mean>=2.77286 58 11 M (0.81034483 0.18965517)
## 8) smoothness_worst>=-1.49649 46 3 M (0.93478261 0.06521739)
## 16) compactness_se< -2.524297 44 1 M (0.97727273 0.02272727)
## 32) compactness_se>=-4.171724 40 0 M (1.00000000 0.00000000) *
## 33) compactness_se< -4.171724 4 1 M (0.75000000 0.25000000)
## 66) texture_mean>=3.068796 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.068796 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.524297 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.49649 12 4 B (0.33333333 0.66666667)
## 18) texture_mean>=3.126045 4 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.126045 8 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.77286 17 6 B (0.35294118 0.64705882)
## 10) symmetry_worst>=-1.195967 6 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.195967 11 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.424186 837 345 B (0.41218638 0.58781362)
## 6) smoothness_mean< -2.23446 698 311 B (0.44555874 0.55444126)
## 12) compactness_se>=-3.466377 133 50 M (0.62406015 0.37593985)
## 24) compactness_se< -3.391153 58 7 M (0.87931034 0.12068966)
## 48) smoothness_mean< -2.262968 55 4 M (0.92727273 0.07272727)
## 96) smoothness_mean>=-2.562637 54 3 M (0.94444444 0.05555556) *
## 97) smoothness_mean< -2.562637 1 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.262968 3 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.391153 75 32 B (0.42666667 0.57333333)
## 50) texture_mean>=3.038537 32 5 M (0.84375000 0.15625000)
## 100) smoothness_worst>=-1.647098 28 2 M (0.92857143 0.07142857) *
## 101) smoothness_worst< -1.647098 4 1 B (0.25000000 0.75000000) *
## 51) texture_mean< 3.038537 43 5 B (0.11627907 0.88372093)
## 102) smoothness_mean>=-2.242902 4 0 M (1.00000000 0.00000000) *
## 103) smoothness_mean< -2.242902 39 1 B (0.02564103 0.97435897) *
## 13) compactness_se< -3.466377 565 228 B (0.40353982 0.59646018)
## 26) smoothness_mean>=-2.251418 27 5 M (0.81481481 0.18518519)
## 52) smoothness_worst>=-1.46195 21 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst< -1.46195 6 1 B (0.16666667 0.83333333)
## 106) texture_mean>=3.037597 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean< 3.037597 5 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.251418 538 206 B (0.38289963 0.61710037)
## 54) smoothness_worst>=-1.424105 15 1 M (0.93333333 0.06666667)
## 108) smoothness_mean>=-2.397334 14 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.397334 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst< -1.424105 523 192 B (0.36711281 0.63288719)
## 110) smoothness_mean< -2.299091 430 177 B (0.41162791 0.58837209) *
## 111) smoothness_mean>=-2.299091 93 15 B (0.16129032 0.83870968) *
## 7) smoothness_mean>=-2.23446 139 34 B (0.24460432 0.75539568)
## 14) texture_mean>=3.209345 4 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.209345 135 30 B (0.22222222 0.77777778)
## 30) symmetry_worst>=-1.765259 62 23 B (0.37096774 0.62903226)
## 60) smoothness_worst>=-1.468303 33 12 M (0.63636364 0.36363636)
## 120) smoothness_worst< -1.423212 14 0 M (1.00000000 0.00000000) *
## 121) smoothness_worst>=-1.423212 19 7 B (0.36842105 0.63157895) *
## 61) smoothness_worst< -1.468303 29 2 B (0.06896552 0.93103448)
## 122) texture_mean>=3.103097 2 0 M (1.00000000 0.00000000) *
## 123) texture_mean< 3.103097 27 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.765259 73 7 B (0.09589041 0.90410959)
## 62) symmetry_worst< -2.354921 4 0 M (1.00000000 0.00000000) *
## 63) symmetry_worst>=-2.354921 69 3 B (0.04347826 0.95652174)
## 126) compactness_se>=-3.02233 1 0 M (1.00000000 0.00000000) *
## 127) compactness_se< -3.02233 68 2 B (0.02941176 0.97058824) *
##
## $trees[[37]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 M (0.53399123 0.46600877)
## 2) texture_worst>=4.50835 620 244 M (0.60645161 0.39354839)
## 4) smoothness_worst< -1.429075 556 204 M (0.63309353 0.36690647)
## 8) smoothness_mean>=-2.566967 546 194 M (0.64468864 0.35531136)
## 16) symmetry_worst< -1.750953 273 76 M (0.72161172 0.27838828)
## 32) symmetry_worst>=-1.862978 66 2 M (0.96969697 0.03030303)
## 64) compactness_se< -3.586422 52 0 M (1.00000000 0.00000000) *
## 65) compactness_se>=-3.586422 14 2 M (0.85714286 0.14285714) *
## 33) symmetry_worst< -1.862978 207 74 M (0.64251208 0.35748792)
## 66) symmetry_worst< -1.931815 173 48 M (0.72254335 0.27745665) *
## 67) symmetry_worst>=-1.931815 34 8 B (0.23529412 0.76470588) *
## 17) symmetry_worst>=-1.750953 273 118 M (0.56776557 0.43223443)
## 34) symmetry_worst>=-1.724518 249 97 M (0.61044177 0.38955823)
## 68) compactness_se< -3.859436 135 35 M (0.74074074 0.25925926) *
## 69) compactness_se>=-3.859436 114 52 B (0.45614035 0.54385965) *
## 35) symmetry_worst< -1.724518 24 3 B (0.12500000 0.87500000)
## 70) texture_worst>=5.020647 3 0 M (1.00000000 0.00000000) *
## 71) texture_worst< 5.020647 21 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.566967 10 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.429075 64 24 B (0.37500000 0.62500000)
## 10) symmetry_worst>=-1.529476 16 1 M (0.93750000 0.06250000)
## 20) smoothness_mean>=-2.347148 15 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean< -2.347148 1 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.529476 48 9 B (0.18750000 0.81250000)
## 22) texture_worst< 4.624204 7 1 M (0.85714286 0.14285714)
## 44) texture_mean>=2.924481 6 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.924481 1 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.624204 41 3 B (0.07317073 0.92682927)
## 46) texture_mean>=3.26885 1 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.26885 40 2 B (0.05000000 0.95000000)
## 94) texture_mean< 2.995481 12 2 B (0.16666667 0.83333333) *
## 95) texture_mean>=2.995481 28 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.50835 292 111 B (0.38013699 0.61986301)
## 6) smoothness_mean< -2.267218 182 85 B (0.46703297 0.53296703)
## 12) smoothness_worst>=-1.496838 49 14 M (0.71428571 0.28571429)
## 24) compactness_se>=-4.122059 40 5 M (0.87500000 0.12500000)
## 48) texture_worst< 4.487999 38 3 M (0.92105263 0.07894737)
## 96) symmetry_worst< -1.440359 35 1 M (0.97142857 0.02857143) *
## 97) symmetry_worst>=-1.440359 3 1 B (0.33333333 0.66666667) *
## 49) texture_worst>=4.487999 2 0 B (0.00000000 1.00000000) *
## 25) compactness_se< -4.122059 9 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.496838 133 50 B (0.37593985 0.62406015)
## 26) symmetry_worst< -1.698675 92 45 B (0.48913043 0.51086957)
## 52) compactness_se>=-3.714667 34 7 M (0.79411765 0.20588235)
## 104) smoothness_mean>=-2.456711 26 2 M (0.92307692 0.07692308) *
## 105) smoothness_mean< -2.456711 8 3 B (0.37500000 0.62500000) *
## 53) compactness_se< -3.714667 58 18 B (0.31034483 0.68965517)
## 106) symmetry_worst>=-1.756915 5 0 M (1.00000000 0.00000000) *
## 107) symmetry_worst< -1.756915 53 13 B (0.24528302 0.75471698) *
## 27) symmetry_worst>=-1.698675 41 5 B (0.12195122 0.87804878)
## 54) compactness_se< -4.281648 10 5 M (0.50000000 0.50000000)
## 108) texture_mean>=2.975525 5 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.975525 5 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-4.281648 31 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.267218 110 26 B (0.23636364 0.76363636)
## 14) smoothness_mean>=-2.172878 34 15 M (0.55882353 0.44117647)
## 28) smoothness_worst< -1.473124 11 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst>=-1.473124 23 8 B (0.34782609 0.65217391)
## 58) symmetry_worst>=-1.596878 10 2 M (0.80000000 0.20000000)
## 116) smoothness_mean>=-2.087477 8 0 M (1.00000000 0.00000000) *
## 117) smoothness_mean< -2.087477 2 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.596878 13 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.172878 76 7 B (0.09210526 0.90789474)
## 30) compactness_se>=-3.084108 6 1 M (0.83333333 0.16666667)
## 60) texture_mean< 2.81718 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.81718 1 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.084108 70 2 B (0.02857143 0.97142857)
## 62) smoothness_worst>=-1.429447 2 0 M (1.00000000 0.00000000) *
## 63) smoothness_worst< -1.429447 68 0 B (0.00000000 1.00000000) *
##
## $trees[[38]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 449 B (0.49232456 0.50767544)
## 2) symmetry_worst>=-1.815934 530 230 M (0.56603774 0.43396226)
## 4) symmetry_worst< -1.809351 26 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.809351 504 230 M (0.54365079 0.45634921)
## 10) compactness_se>=-4.547852 473 206 M (0.56448203 0.43551797)
## 20) texture_mean>=2.929857 317 119 M (0.62460568 0.37539432)
## 40) texture_worst< 4.786372 163 41 M (0.74846626 0.25153374)
## 80) compactness_se>=-4.291103 157 35 M (0.77707006 0.22292994) *
## 81) compactness_se< -4.291103 6 0 B (0.00000000 1.00000000) *
## 41) texture_worst>=4.786372 154 76 B (0.49350649 0.50649351)
## 82) texture_worst>=4.818867 130 55 M (0.57692308 0.42307692) *
## 83) texture_worst< 4.818867 24 1 B (0.04166667 0.95833333) *
## 21) texture_mean< 2.929857 156 69 B (0.44230769 0.55769231)
## 42) texture_mean< 2.899771 123 55 M (0.55284553 0.44715447)
## 84) compactness_se>=-3.982052 93 31 M (0.66666667 0.33333333) *
## 85) compactness_se< -3.982052 30 6 B (0.20000000 0.80000000) *
## 43) texture_mean>=2.899771 33 1 B (0.03030303 0.96969697)
## 86) symmetry_worst>=-1.383772 1 0 M (1.00000000 0.00000000) *
## 87) symmetry_worst< -1.383772 32 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.547852 31 7 B (0.22580645 0.77419355)
## 22) texture_worst< 4.622562 11 4 M (0.63636364 0.36363636)
## 44) texture_mean>=2.912851 7 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.912851 4 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.622562 20 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.815934 382 149 B (0.39005236 0.60994764)
## 6) texture_worst>=4.897936 87 31 M (0.64367816 0.35632184)
## 12) symmetry_worst>=-2.207988 69 14 M (0.79710145 0.20289855)
## 24) texture_mean< 3.361554 63 8 M (0.87301587 0.12698413)
## 48) compactness_se>=-4.758524 59 4 M (0.93220339 0.06779661)
## 96) smoothness_worst>=-1.62752 57 2 M (0.96491228 0.03508772) *
## 97) smoothness_worst< -1.62752 2 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.758524 4 0 B (0.00000000 1.00000000) *
## 25) texture_mean>=3.361554 6 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -2.207988 18 1 B (0.05555556 0.94444444)
## 26) smoothness_mean>=-2.282229 1 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean< -2.282229 17 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.897936 295 93 B (0.31525424 0.68474576)
## 14) smoothness_worst< -1.474648 228 90 B (0.39473684 0.60526316)
## 28) smoothness_worst>=-1.482898 24 3 M (0.87500000 0.12500000)
## 56) compactness_se>=-3.967101 22 1 M (0.95454545 0.04545455)
## 112) texture_mean>=2.721909 21 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.721909 1 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -3.967101 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.482898 204 69 B (0.33823529 0.66176471)
## 58) texture_worst>=4.575448 70 34 M (0.51428571 0.48571429)
## 116) smoothness_worst>=-1.580846 51 15 M (0.70588235 0.29411765) *
## 117) smoothness_worst< -1.580846 19 0 B (0.00000000 1.00000000) *
## 59) texture_worst< 4.575448 134 33 B (0.24626866 0.75373134)
## 118) smoothness_worst< -1.595961 47 22 M (0.53191489 0.46808511) *
## 119) smoothness_worst>=-1.595961 87 8 B (0.09195402 0.90804598) *
## 15) smoothness_worst>=-1.474648 67 3 B (0.04477612 0.95522388)
## 30) smoothness_mean< -2.352223 8 3 B (0.37500000 0.62500000)
## 60) texture_mean>=2.830895 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.830895 5 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.352223 59 0 B (0.00000000 1.00000000) *
##
## $trees[[39]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 380 B (0.41666667 0.58333333)
## 2) smoothness_mean>=-2.423454 691 318 B (0.46020260 0.53979740)
## 4) texture_worst>=4.389172 505 243 M (0.51881188 0.48118812)
## 8) texture_mean< 3.35917 484 222 M (0.54132231 0.45867769)
## 16) smoothness_mean< -2.093138 465 203 M (0.56344086 0.43655914)
## 32) symmetry_worst>=-2.052205 355 134 M (0.62253521 0.37746479)
## 64) texture_mean>=3.054236 107 22 M (0.79439252 0.20560748) *
## 65) texture_mean< 3.054236 248 112 M (0.54838710 0.45161290) *
## 33) symmetry_worst< -2.052205 110 41 B (0.37272727 0.62727273)
## 66) symmetry_worst< -2.115205 77 36 M (0.53246753 0.46753247) *
## 67) symmetry_worst>=-2.115205 33 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.093138 19 0 B (0.00000000 1.00000000) *
## 9) texture_mean>=3.35917 21 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.389172 186 56 B (0.30107527 0.69892473)
## 10) texture_worst< 4.313991 135 50 B (0.37037037 0.62962963)
## 20) symmetry_worst>=-1.828847 77 38 M (0.50649351 0.49350649)
## 40) compactness_se>=-3.647113 36 9 M (0.75000000 0.25000000)
## 80) texture_mean< 2.801532 25 2 M (0.92000000 0.08000000) *
## 81) texture_mean>=2.801532 11 4 B (0.36363636 0.63636364) *
## 41) compactness_se< -3.647113 41 12 B (0.29268293 0.70731707)
## 82) smoothness_mean< -2.411844 9 1 M (0.88888889 0.11111111) *
## 83) smoothness_mean>=-2.411844 32 4 B (0.12500000 0.87500000) *
## 21) symmetry_worst< -1.828847 58 11 B (0.18965517 0.81034483)
## 42) compactness_se< -3.57366 23 11 B (0.47826087 0.52173913)
## 84) smoothness_worst>=-1.499666 10 1 M (0.90000000 0.10000000) *
## 85) smoothness_worst< -1.499666 13 2 B (0.15384615 0.84615385) *
## 43) compactness_se>=-3.57366 35 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.313991 51 6 B (0.11764706 0.88235294)
## 22) smoothness_worst>=-1.428351 2 0 M (1.00000000 0.00000000) *
## 23) smoothness_worst< -1.428351 49 4 B (0.08163265 0.91836735)
## 46) compactness_se>=-3.095053 8 3 B (0.37500000 0.62500000)
## 92) texture_mean>=2.911683 3 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.911683 5 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.095053 41 1 B (0.02439024 0.97560976)
## 94) symmetry_worst< -1.973981 8 1 B (0.12500000 0.87500000) *
## 95) symmetry_worst>=-1.973981 33 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.423454 221 62 B (0.28054299 0.71945701)
## 6) symmetry_worst>=-1.496954 15 3 M (0.80000000 0.20000000)
## 12) texture_mean>=2.977947 12 0 M (1.00000000 0.00000000) *
## 13) texture_mean< 2.977947 3 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.496954 206 50 B (0.24271845 0.75728155)
## 14) texture_mean>=3.388429 7 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.388429 199 43 B (0.21608040 0.78391960)
## 30) compactness_se< -4.687525 23 9 M (0.60869565 0.39130435)
## 60) compactness_se>=-4.706178 14 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.687525 176 29 B (0.16477273 0.83522727)
## 62) texture_worst< 3.96146 12 3 M (0.75000000 0.25000000)
## 124) texture_mean>=2.754513 9 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.754513 3 0 B (0.00000000 1.00000000) *
## 63) texture_worst>=3.96146 164 20 B (0.12195122 0.87804878)
## 126) smoothness_worst< -1.720903 4 1 M (0.75000000 0.25000000) *
## 127) smoothness_worst>=-1.720903 160 17 B (0.10625000 0.89375000) *
##
## $trees[[40]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 348 B (0.38157895 0.61842105)
## 2) symmetry_worst>=-1.366937 40 10 M (0.75000000 0.25000000)
## 4) symmetry_worst< -1.23578 22 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-1.23578 18 8 B (0.44444444 0.55555556)
## 10) smoothness_worst>=-1.451731 7 0 M (1.00000000 0.00000000) *
## 11) smoothness_worst< -1.451731 11 1 B (0.09090909 0.90909091)
## 22) texture_mean>=3.158816 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.158816 10 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.366937 872 318 B (0.36467890 0.63532110)
## 6) smoothness_worst< -1.439482 757 296 B (0.39101717 0.60898283)
## 12) symmetry_worst>=-2.232873 679 283 B (0.41678940 0.58321060)
## 24) symmetry_worst< -1.634569 467 218 B (0.46680942 0.53319058)
## 48) smoothness_worst>=-1.604472 410 200 M (0.51219512 0.48780488)
## 96) smoothness_mean< -2.349846 218 86 M (0.60550459 0.39449541) *
## 97) smoothness_mean>=-2.349846 192 78 B (0.40625000 0.59375000) *
## 49) smoothness_worst< -1.604472 57 8 B (0.14035088 0.85964912)
## 98) texture_mean< 2.966301 13 5 B (0.38461538 0.61538462) *
## 99) texture_mean>=2.966301 44 3 B (0.06818182 0.93181818) *
## 25) symmetry_worst>=-1.634569 212 65 B (0.30660377 0.69339623)
## 50) symmetry_worst>=-1.549706 109 48 B (0.44036697 0.55963303)
## 100) smoothness_worst< -1.513087 42 13 M (0.69047619 0.30952381) *
## 101) smoothness_worst>=-1.513087 67 19 B (0.28358209 0.71641791) *
## 51) symmetry_worst< -1.549706 103 17 B (0.16504854 0.83495146)
## 102) smoothness_worst>=-1.470752 10 2 M (0.80000000 0.20000000) *
## 103) smoothness_worst< -1.470752 93 9 B (0.09677419 0.90322581) *
## 13) symmetry_worst< -2.232873 78 13 B (0.16666667 0.83333333)
## 26) smoothness_mean>=-2.307549 28 13 B (0.46428571 0.53571429)
## 52) smoothness_worst< -1.59459 11 0 M (1.00000000 0.00000000) *
## 53) smoothness_worst>=-1.59459 17 2 B (0.11764706 0.88235294)
## 106) texture_worst>=4.618547 2 0 M (1.00000000 0.00000000) *
## 107) texture_worst< 4.618547 15 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean< -2.307549 50 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.439482 115 22 B (0.19130435 0.80869565)
## 14) smoothness_mean>=-2.079457 7 1 M (0.85714286 0.14285714)
## 28) smoothness_mean< -1.889548 6 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-1.889548 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.079457 108 16 B (0.14814815 0.85185185)
## 30) texture_mean>=3.242184 4 0 M (1.00000000 0.00000000) *
## 31) texture_mean< 3.242184 104 12 B (0.11538462 0.88461538)
## 62) compactness_se< -4.38342 8 3 M (0.62500000 0.37500000)
## 124) texture_mean>=2.904788 5 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.904788 3 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-4.38342 96 7 B (0.07291667 0.92708333)
## 126) smoothness_worst>=-1.334845 1 0 M (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.334845 95 6 B (0.06315789 0.93684211) *
##
## $trees[[41]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 368 B (0.40350877 0.59649123)
## 2) smoothness_worst< -1.434076 810 344 B (0.42469136 0.57530864)
## 4) smoothness_worst>=-1.482699 221 102 M (0.53846154 0.46153846)
## 8) symmetry_worst>=-1.721298 118 33 M (0.72033898 0.27966102)
## 16) symmetry_worst< -1.244631 110 25 M (0.77272727 0.22727273)
## 32) compactness_se>=-4.512898 104 19 M (0.81730769 0.18269231)
## 64) compactness_se>=-3.465064 28 0 M (1.00000000 0.00000000) *
## 65) compactness_se< -3.465064 76 19 M (0.75000000 0.25000000) *
## 33) compactness_se< -4.512898 6 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.244631 8 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.721298 103 34 B (0.33009709 0.66990291)
## 18) smoothness_worst< -1.480334 14 0 M (1.00000000 0.00000000) *
## 19) smoothness_worst>=-1.480334 89 20 B (0.22471910 0.77528090)
## 38) texture_worst>=4.877645 19 4 M (0.78947368 0.21052632)
## 76) compactness_se>=-4.054503 15 0 M (1.00000000 0.00000000) *
## 77) compactness_se< -4.054503 4 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 4.877645 70 5 B (0.07142857 0.92857143)
## 78) smoothness_worst< -1.477193 5 2 B (0.40000000 0.60000000) *
## 79) smoothness_worst>=-1.477193 65 3 B (0.04615385 0.95384615) *
## 5) smoothness_worst< -1.482699 589 225 B (0.38200340 0.61799660)
## 10) compactness_se< -4.49319 77 31 M (0.59740260 0.40259740)
## 20) compactness_se>=-4.779408 65 19 M (0.70769231 0.29230769)
## 40) texture_worst>=4.712681 30 1 M (0.96666667 0.03333333)
## 80) texture_mean< 3.262924 29 0 M (1.00000000 0.00000000) *
## 81) texture_mean>=3.262924 1 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.712681 35 17 B (0.48571429 0.51428571)
## 82) smoothness_mean>=-2.422683 17 3 M (0.82352941 0.17647059) *
## 83) smoothness_mean< -2.422683 18 3 B (0.16666667 0.83333333) *
## 21) compactness_se< -4.779408 12 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.49319 512 179 B (0.34960938 0.65039062)
## 22) compactness_se>=-3.721197 227 102 B (0.44933921 0.55066079)
## 44) compactness_se< -3.657776 18 1 M (0.94444444 0.05555556)
## 88) texture_worst< 4.828083 15 0 M (1.00000000 0.00000000) *
## 89) texture_worst>=4.828083 3 1 M (0.66666667 0.33333333) *
## 45) compactness_se>=-3.657776 209 85 B (0.40669856 0.59330144)
## 90) texture_mean>=3.035431 87 36 M (0.58620690 0.41379310) *
## 91) texture_mean< 3.035431 122 34 B (0.27868852 0.72131148) *
## 23) compactness_se< -3.721197 285 77 B (0.27017544 0.72982456)
## 46) compactness_se< -3.869459 226 77 B (0.34070796 0.65929204)
## 92) texture_worst>=5.001873 32 8 M (0.75000000 0.25000000) *
## 93) texture_worst< 5.001873 194 53 B (0.27319588 0.72680412) *
## 47) compactness_se>=-3.869459 59 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.434076 102 24 B (0.23529412 0.76470588)
## 6) symmetry_worst>=-1.270655 5 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.270655 97 19 B (0.19587629 0.80412371)
## 14) compactness_se>=-2.695649 2 0 M (1.00000000 0.00000000) *
## 15) compactness_se< -2.695649 95 17 B (0.17894737 0.82105263)
## 30) compactness_se< -4.186419 5 2 M (0.60000000 0.40000000)
## 60) texture_mean>=2.950291 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.950291 2 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.186419 90 14 B (0.15555556 0.84444444)
## 62) compactness_se>=-3.998097 59 14 B (0.23728814 0.76271186)
## 124) compactness_se< -3.768789 7 1 M (0.85714286 0.14285714) *
## 125) compactness_se>=-3.768789 52 8 B (0.15384615 0.84615385) *
## 63) compactness_se< -3.998097 31 0 B (0.00000000 1.00000000) *
##
## $trees[[42]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 409 B (0.44846491 0.55153509)
## 2) texture_worst< 4.067485 63 18 M (0.71428571 0.28571429)
## 4) texture_mean>=2.650752 45 6 M (0.86666667 0.13333333)
## 8) compactness_se< -3.48221 39 2 M (0.94871795 0.05128205)
## 16) smoothness_mean>=-2.466148 38 1 M (0.97368421 0.02631579)
## 32) compactness_se>=-4.044115 37 0 M (1.00000000 0.00000000) *
## 33) compactness_se< -4.044115 1 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.466148 1 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-3.48221 6 2 B (0.33333333 0.66666667)
## 18) texture_mean< 2.690691 2 0 M (1.00000000 0.00000000) *
## 19) texture_mean>=2.690691 4 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.650752 18 6 B (0.33333333 0.66666667)
## 10) texture_mean< 2.525679 6 0 M (1.00000000 0.00000000) *
## 11) texture_mean>=2.525679 12 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.067485 849 364 B (0.42873969 0.57126031)
## 6) symmetry_worst>=-2.01934 707 327 B (0.46251768 0.53748232)
## 12) symmetry_worst< -1.990832 26 1 M (0.96153846 0.03846154)
## 24) smoothness_mean< -2.261392 22 0 M (1.00000000 0.00000000) *
## 25) smoothness_mean>=-2.261392 4 1 M (0.75000000 0.25000000)
## 50) texture_mean>=2.960755 3 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.960755 1 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.990832 681 302 B (0.44346549 0.55653451)
## 26) texture_mean>=2.876103 534 261 B (0.48876404 0.51123596)
## 52) texture_mean< 2.893423 33 2 M (0.93939394 0.06060606)
## 104) symmetry_worst< -1.684827 30 0 M (1.00000000 0.00000000) *
## 105) symmetry_worst>=-1.684827 3 1 B (0.33333333 0.66666667) *
## 53) texture_mean>=2.893423 501 230 B (0.45908184 0.54091816)
## 106) smoothness_mean< -2.114071 473 229 B (0.48414376 0.51585624) *
## 107) smoothness_mean>=-2.114071 28 1 B (0.03571429 0.96428571) *
## 27) texture_mean< 2.876103 147 41 B (0.27891156 0.72108844)
## 54) smoothness_mean>=-2.15202 11 1 M (0.90909091 0.09090909)
## 108) compactness_se>=-3.643968 10 0 M (1.00000000 0.00000000) *
## 109) compactness_se< -3.643968 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_mean< -2.15202 136 31 B (0.22794118 0.77205882)
## 110) texture_mean< 2.841997 86 29 B (0.33720930 0.66279070) *
## 111) texture_mean>=2.841997 50 2 B (0.04000000 0.96000000) *
## 7) symmetry_worst< -2.01934 142 37 B (0.26056338 0.73943662)
## 14) smoothness_mean>=-2.394379 83 30 B (0.36144578 0.63855422)
## 28) smoothness_mean< -2.382983 13 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.382983 70 17 B (0.24285714 0.75714286)
## 58) compactness_se< -4.492707 5 0 M (1.00000000 0.00000000) *
## 59) compactness_se>=-4.492707 65 12 B (0.18461538 0.81538462)
## 118) smoothness_worst>=-1.518057 31 12 B (0.38709677 0.61290323) *
## 119) smoothness_worst< -1.518057 34 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.394379 59 7 B (0.11864407 0.88135593)
## 30) compactness_se>=-3.530926 10 3 M (0.70000000 0.30000000)
## 60) compactness_se< -3.188171 6 0 M (1.00000000 0.00000000) *
## 61) compactness_se>=-3.188171 4 1 B (0.25000000 0.75000000)
## 122) texture_worst< 4.59095 1 0 M (1.00000000 0.00000000) *
## 123) texture_worst>=4.59095 3 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.530926 49 0 B (0.00000000 1.00000000) *
##
## $trees[[43]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 424 M (0.53508772 0.46491228)
## 2) symmetry_worst>=-2.052205 766 326 M (0.57441253 0.42558747)
## 4) smoothness_worst< -1.363268 747 310 M (0.58500669 0.41499331)
## 8) compactness_se>=-3.721197 323 108 M (0.66563467 0.33436533)
## 16) smoothness_mean>=-2.325189 173 39 M (0.77456647 0.22543353)
## 32) compactness_se< -3.494301 68 4 M (0.94117647 0.05882353)
## 64) texture_mean>=2.649195 65 1 M (0.98461538 0.01538462) *
## 65) texture_mean< 2.649195 3 0 B (0.00000000 1.00000000) *
## 33) compactness_se>=-3.494301 105 35 M (0.66666667 0.33333333)
## 66) compactness_se>=-3.447524 93 23 M (0.75268817 0.24731183) *
## 67) compactness_se< -3.447524 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.325189 150 69 M (0.54000000 0.46000000)
## 34) smoothness_mean< -2.326622 135 54 M (0.60000000 0.40000000)
## 68) smoothness_worst>=-1.50451 32 3 M (0.90625000 0.09375000) *
## 69) smoothness_worst< -1.50451 103 51 M (0.50485437 0.49514563) *
## 35) smoothness_mean>=-2.326622 15 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.721197 424 202 M (0.52358491 0.47641509)
## 18) compactness_se< -3.734107 402 180 M (0.55223881 0.44776119)
## 36) smoothness_worst< -1.429075 368 154 M (0.58152174 0.41847826)
## 72) smoothness_worst>=-1.446808 35 3 M (0.91428571 0.08571429) *
## 73) smoothness_worst< -1.446808 333 151 M (0.54654655 0.45345345) *
## 37) smoothness_worst>=-1.429075 34 8 B (0.23529412 0.76470588)
## 74) smoothness_mean>=-2.15812 3 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean< -2.15812 31 5 B (0.16129032 0.83870968) *
## 19) compactness_se>=-3.734107 22 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.363268 19 3 B (0.15789474 0.84210526)
## 10) compactness_se< -3.488238 5 2 M (0.60000000 0.40000000)
## 20) texture_mean>=2.688296 3 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-3.488238 14 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.052205 146 48 B (0.32876712 0.67123288)
## 6) texture_worst< 5.15236 119 48 B (0.40336134 0.59663866)
## 12) symmetry_worst< -2.111279 97 47 B (0.48453608 0.51546392)
## 24) symmetry_worst>=-2.191305 32 8 M (0.75000000 0.25000000)
## 48) smoothness_mean>=-2.408231 25 1 M (0.96000000 0.04000000)
## 96) compactness_se< -3.495845 23 0 M (1.00000000 0.00000000) *
## 97) compactness_se>=-3.495845 2 1 M (0.50000000 0.50000000) *
## 49) smoothness_mean< -2.408231 7 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -2.191305 65 23 B (0.35384615 0.64615385)
## 50) smoothness_mean>=-2.225534 6 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.225534 59 17 B (0.28813559 0.71186441)
## 102) compactness_se< -4.564659 8 1 M (0.87500000 0.12500000) *
## 103) compactness_se>=-4.564659 51 10 B (0.19607843 0.80392157) *
## 13) symmetry_worst>=-2.111279 22 1 B (0.04545455 0.95454545)
## 26) smoothness_mean< -2.576965 1 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean>=-2.576965 21 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=5.15236 27 0 B (0.00000000 1.00000000) *
##
## $trees[[44]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 431 B (0.47258772 0.52741228)
## 2) symmetry_worst>=-1.36527 34 3 M (0.91176471 0.08823529)
## 4) smoothness_worst>=-1.49848 25 0 M (1.00000000 0.00000000) *
## 5) smoothness_worst< -1.49848 9 3 M (0.66666667 0.33333333)
## 10) smoothness_mean< -2.372457 6 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean>=-2.372457 3 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.36527 878 400 B (0.45558087 0.54441913)
## 6) compactness_se< -3.355844 760 366 B (0.48157895 0.51842105)
## 12) texture_worst< 4.740988 495 230 M (0.53535354 0.46464646)
## 24) texture_worst>=4.737862 26 0 M (1.00000000 0.00000000) *
## 25) texture_worst< 4.737862 469 230 M (0.50959488 0.49040512)
## 50) compactness_se>=-3.705619 178 62 M (0.65168539 0.34831461)
## 100) smoothness_worst< -1.477785 136 30 M (0.77941176 0.22058824) *
## 101) smoothness_worst>=-1.477785 42 10 B (0.23809524 0.76190476) *
## 51) compactness_se< -3.705619 291 123 B (0.42268041 0.57731959)
## 102) smoothness_worst>=-1.451541 42 9 M (0.78571429 0.21428571) *
## 103) smoothness_worst< -1.451541 249 90 B (0.36144578 0.63855422) *
## 13) texture_worst>=4.740988 265 101 B (0.38113208 0.61886792)
## 26) smoothness_worst>=-1.52112 122 52 M (0.57377049 0.42622951)
## 52) texture_worst>=4.821213 101 33 M (0.67326733 0.32673267)
## 104) texture_worst< 5.163886 71 12 M (0.83098592 0.16901408) *
## 105) texture_worst>=5.163886 30 9 B (0.30000000 0.70000000) *
## 53) texture_worst< 4.821213 21 2 B (0.09523810 0.90476190)
## 106) smoothness_mean>=-2.208092 2 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.208092 19 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst< -1.52112 143 31 B (0.21678322 0.78321678)
## 54) compactness_se< -4.49816 25 10 M (0.60000000 0.40000000)
## 108) compactness_se>=-4.706178 16 1 M (0.93750000 0.06250000) *
## 109) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 55) compactness_se>=-4.49816 118 16 B (0.13559322 0.86440678)
## 110) smoothness_mean>=-2.369574 34 14 B (0.41176471 0.58823529) *
## 111) smoothness_mean< -2.369574 84 2 B (0.02380952 0.97619048) *
## 7) compactness_se>=-3.355844 118 34 B (0.28813559 0.71186441)
## 14) texture_mean>=3.039251 38 11 M (0.71052632 0.28947368)
## 28) texture_worst< 4.980233 21 1 M (0.95238095 0.04761905)
## 56) smoothness_mean>=-2.479915 19 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.479915 2 1 M (0.50000000 0.50000000)
## 114) texture_mean>=3.072866 1 0 M (1.00000000 0.00000000) *
## 115) texture_mean< 3.072866 1 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.980233 17 7 B (0.41176471 0.58823529)
## 58) texture_worst>=5.016194 6 0 M (1.00000000 0.00000000) *
## 59) texture_worst< 5.016194 11 1 B (0.09090909 0.90909091)
## 118) texture_mean< 3.154646 1 0 M (1.00000000 0.00000000) *
## 119) texture_mean>=3.154646 10 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.039251 80 7 B (0.08750000 0.91250000)
## 30) smoothness_worst>=-1.387398 7 2 M (0.71428571 0.28571429)
## 60) texture_mean>=2.701935 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.701935 2 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.387398 73 2 B (0.02739726 0.97260274)
## 62) texture_mean>=3.031099 5 1 B (0.20000000 0.80000000)
## 124) texture_mean< 3.032546 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean>=3.032546 4 0 B (0.00000000 1.00000000) *
## 63) texture_mean< 3.031099 68 1 B (0.01470588 0.98529412)
## 126) symmetry_worst>=-1.474719 9 1 B (0.11111111 0.88888889) *
## 127) symmetry_worst< -1.474719 59 0 B (0.00000000 1.00000000) *
##
## $trees[[45]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_worst< 4.642157 551 235 M (0.57350272 0.42649728)
## 4) texture_mean>=2.96604 155 36 M (0.76774194 0.23225806)
## 8) texture_worst>=4.354728 150 31 M (0.79333333 0.20666667)
## 16) compactness_se< -2.807696 141 25 M (0.82269504 0.17730496)
## 32) compactness_se>=-4.291103 127 18 M (0.85826772 0.14173228)
## 64) texture_mean< 3.138519 124 15 M (0.87903226 0.12096774) *
## 65) texture_mean>=3.138519 3 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.291103 14 7 M (0.50000000 0.50000000)
## 66) symmetry_worst< -1.7426 9 2 M (0.77777778 0.22222222) *
## 67) symmetry_worst>=-1.7426 5 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.807696 9 3 B (0.33333333 0.66666667)
## 34) texture_mean>=3.003947 3 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.003947 6 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.354728 5 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.96604 396 197 B (0.49747475 0.50252525)
## 10) smoothness_worst< -1.470556 309 139 M (0.55016181 0.44983819)
## 20) texture_mean< 2.940483 268 106 M (0.60447761 0.39552239)
## 40) compactness_se>=-4.701576 257 95 M (0.63035019 0.36964981)
## 80) smoothness_mean>=-2.50355 248 86 M (0.65322581 0.34677419) *
## 81) smoothness_mean< -2.50355 9 0 B (0.00000000 1.00000000) *
## 41) compactness_se< -4.701576 11 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=2.940483 41 8 B (0.19512195 0.80487805)
## 42) smoothness_worst>=-1.506664 11 3 M (0.72727273 0.27272727)
## 84) texture_mean>=2.955938 8 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 2.955938 3 0 B (0.00000000 1.00000000) *
## 43) smoothness_worst< -1.506664 30 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.470556 87 27 B (0.31034483 0.68965517)
## 22) smoothness_worst>=-1.448697 51 25 M (0.50980392 0.49019608)
## 44) smoothness_worst< -1.440419 18 2 M (0.88888889 0.11111111)
## 88) texture_mean>=2.801549 16 0 M (1.00000000 0.00000000) *
## 89) texture_mean< 2.801549 2 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst>=-1.440419 33 10 B (0.30303030 0.69696970)
## 90) compactness_se>=-3.300819 5 0 M (1.00000000 0.00000000) *
## 91) compactness_se< -3.300819 28 5 B (0.17857143 0.82142857) *
## 23) smoothness_worst< -1.448697 36 1 B (0.02777778 0.97222222)
## 46) texture_worst< 4.017902 2 1 M (0.50000000 0.50000000)
## 92) texture_mean>=2.587173 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.587173 1 0 B (0.00000000 1.00000000) *
## 47) texture_worst>=4.017902 34 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.642157 361 154 B (0.42659280 0.57340720)
## 6) symmetry_worst>=-1.551105 74 24 M (0.67567568 0.32432432)
## 12) compactness_se>=-4.507761 68 18 M (0.73529412 0.26470588)
## 24) compactness_se< -3.135699 58 11 M (0.81034483 0.18965517)
## 48) texture_worst>=4.82155 27 0 M (1.00000000 0.00000000) *
## 49) texture_worst< 4.82155 31 11 M (0.64516129 0.35483871)
## 98) texture_worst< 4.789782 20 0 M (1.00000000 0.00000000) *
## 99) texture_worst>=4.789782 11 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.135699 10 3 B (0.30000000 0.70000000)
## 50) smoothness_mean>=-2.336585 3 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean< -2.336585 7 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.507761 6 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.551105 287 104 B (0.36236934 0.63763066)
## 14) compactness_se< -4.434687 31 8 M (0.74193548 0.25806452)
## 28) symmetry_worst>=-1.912217 23 2 M (0.91304348 0.08695652)
## 56) texture_mean>=2.915217 21 0 M (1.00000000 0.00000000) *
## 57) texture_mean< 2.915217 2 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst< -1.912217 8 2 B (0.25000000 0.75000000)
## 58) smoothness_worst>=-1.555243 3 1 M (0.66666667 0.33333333)
## 116) texture_mean< 3.213341 2 0 M (1.00000000 0.00000000) *
## 117) texture_mean>=3.213341 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst< -1.555243 5 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.434687 256 81 B (0.31640625 0.68359375)
## 30) smoothness_mean>=-2.394379 176 69 B (0.39204545 0.60795455)
## 60) smoothness_worst< -1.484675 83 34 M (0.59036145 0.40963855)
## 120) symmetry_worst>=-2.207988 67 18 M (0.73134328 0.26865672) *
## 121) symmetry_worst< -2.207988 16 0 B (0.00000000 1.00000000) *
## 61) smoothness_worst>=-1.484675 93 20 B (0.21505376 0.78494624)
## 122) compactness_se>=-3.300427 7 0 M (1.00000000 0.00000000) *
## 123) compactness_se< -3.300427 86 13 B (0.15116279 0.84883721) *
## 31) smoothness_mean< -2.394379 80 12 B (0.15000000 0.85000000)
## 62) smoothness_worst>=-1.522574 20 9 B (0.45000000 0.55000000)
## 124) smoothness_mean< -2.439503 11 2 M (0.81818182 0.18181818) *
## 125) smoothness_mean>=-2.439503 9 0 B (0.00000000 1.00000000) *
## 63) smoothness_worst< -1.522574 60 3 B (0.05000000 0.95000000)
## 126) symmetry_worst< -2.242858 1 0 M (1.00000000 0.00000000) *
## 127) symmetry_worst>=-2.242858 59 2 B (0.03389831 0.96610169) *
##
## $trees[[46]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 B (0.46600877 0.53399123)
## 2) smoothness_mean>=-2.367658 500 235 M (0.53000000 0.47000000)
## 4) smoothness_mean< -2.349943 56 8 M (0.85714286 0.14285714)
## 8) symmetry_worst< -1.528454 49 1 M (0.97959184 0.02040816)
## 16) compactness_se>=-4.721249 48 0 M (1.00000000 0.00000000) *
## 17) compactness_se< -4.721249 1 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.528454 7 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.349943 444 217 B (0.48873874 0.51126126)
## 10) compactness_se>=-4.025757 329 141 M (0.57142857 0.42857143)
## 20) compactness_se< -3.494301 211 65 M (0.69194313 0.30805687)
## 40) texture_worst>=4.907333 47 2 M (0.95744681 0.04255319)
## 80) symmetry_worst>=-2.207988 45 0 M (1.00000000 0.00000000) *
## 81) symmetry_worst< -2.207988 2 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.907333 164 63 M (0.61585366 0.38414634)
## 82) texture_worst< 4.608306 139 43 M (0.69064748 0.30935252) *
## 83) texture_worst>=4.608306 25 5 B (0.20000000 0.80000000) *
## 21) compactness_se>=-3.494301 118 42 B (0.35593220 0.64406780)
## 42) compactness_se>=-3.445472 87 42 B (0.48275862 0.51724138)
## 84) texture_worst>=4.558285 30 7 M (0.76666667 0.23333333) *
## 85) texture_worst< 4.558285 57 19 B (0.33333333 0.66666667) *
## 43) compactness_se< -3.445472 31 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.025757 115 29 B (0.25217391 0.74782609)
## 22) smoothness_mean< -2.290664 41 13 M (0.68292683 0.31707317)
## 44) smoothness_mean>=-2.333927 34 6 M (0.82352941 0.17647059)
## 88) compactness_se>=-4.656191 31 3 M (0.90322581 0.09677419) *
## 89) compactness_se< -4.656191 3 0 B (0.00000000 1.00000000) *
## 45) smoothness_mean< -2.333927 7 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.290664 74 1 B (0.01351351 0.98648649)
## 46) smoothness_mean>=-2.21595 16 1 B (0.06250000 0.93750000)
## 92) smoothness_mean< -2.210016 2 1 M (0.50000000 0.50000000) *
## 93) smoothness_mean>=-2.210016 14 0 B (0.00000000 1.00000000) *
## 47) smoothness_mean< -2.21595 58 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.367658 412 160 B (0.38834951 0.61165049)
## 6) compactness_se< -4.098964 197 94 M (0.52284264 0.47715736)
## 12) compactness_se>=-4.260936 51 4 M (0.92156863 0.07843137)
## 24) smoothness_worst>=-1.667778 49 2 M (0.95918367 0.04081633)
## 48) smoothness_worst< -1.458133 48 1 M (0.97916667 0.02083333)
## 96) smoothness_mean>=-2.440377 41 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean< -2.440377 7 1 M (0.85714286 0.14285714) *
## 49) smoothness_worst>=-1.458133 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.667778 2 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.260936 146 56 B (0.38356164 0.61643836)
## 26) compactness_se< -4.658767 28 6 M (0.78571429 0.21428571)
## 52) compactness_se>=-4.737326 22 0 M (1.00000000 0.00000000) *
## 53) compactness_se< -4.737326 6 0 B (0.00000000 1.00000000) *
## 27) compactness_se>=-4.658767 118 34 B (0.28813559 0.71186441)
## 54) compactness_se>=-4.356557 53 26 B (0.49056604 0.50943396)
## 108) smoothness_mean< -2.45841 24 3 M (0.87500000 0.12500000) *
## 109) smoothness_mean>=-2.45841 29 5 B (0.17241379 0.82758621) *
## 55) compactness_se< -4.356557 65 8 B (0.12307692 0.87692308)
## 110) texture_mean>=2.985131 30 8 B (0.26666667 0.73333333) *
## 111) texture_mean< 2.985131 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-4.098964 215 57 B (0.26511628 0.73488372)
## 14) texture_worst>=4.569119 116 46 B (0.39655172 0.60344828)
## 28) texture_worst< 4.683744 43 13 M (0.69767442 0.30232558)
## 56) smoothness_worst< -1.532274 26 3 M (0.88461538 0.11538462)
## 112) texture_mean< 3.146714 24 1 M (0.95833333 0.04166667) *
## 113) texture_mean>=3.146714 2 0 B (0.00000000 1.00000000) *
## 57) smoothness_worst>=-1.532274 17 7 B (0.41176471 0.58823529)
## 114) texture_mean>=2.955415 7 0 M (1.00000000 0.00000000) *
## 115) texture_mean< 2.955415 10 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.683744 73 16 B (0.21917808 0.78082192)
## 58) texture_mean>=3.428781 3 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.428781 70 13 B (0.18571429 0.81428571)
## 118) symmetry_worst>=-1.541072 5 1 M (0.80000000 0.20000000) *
## 119) symmetry_worst< -1.541072 65 9 B (0.13846154 0.86153846) *
## 15) texture_worst< 4.569119 99 11 B (0.11111111 0.88888889)
## 30) smoothness_mean>=-2.376139 4 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -2.376139 95 7 B (0.07368421 0.92631579)
## 62) smoothness_worst>=-1.455747 2 0 M (1.00000000 0.00000000) *
## 63) smoothness_worst< -1.455747 93 5 B (0.05376344 0.94623656)
## 126) texture_worst< 3.981964 11 4 B (0.36363636 0.63636364) *
## 127) texture_worst>=3.981964 82 1 B (0.01219512 0.98780488) *
##
## $trees[[47]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 432 B (0.47368421 0.52631579)
## 2) texture_mean>=2.960364 442 186 M (0.57918552 0.42081448)
## 4) smoothness_mean< -2.106736 422 168 M (0.60189573 0.39810427)
## 8) compactness_se>=-4.275512 330 116 M (0.64848485 0.35151515)
## 16) smoothness_worst>=-1.618016 308 100 M (0.67532468 0.32467532)
## 32) compactness_se< -4.096569 32 1 M (0.96875000 0.03125000)
## 64) symmetry_worst>=-2.253809 31 0 M (1.00000000 0.00000000) *
## 65) symmetry_worst< -2.253809 1 0 B (0.00000000 1.00000000) *
## 33) compactness_se>=-4.096569 276 99 M (0.64130435 0.35869565)
## 66) compactness_se>=-4.05446 262 85 M (0.67557252 0.32442748) *
## 67) compactness_se< -4.05446 14 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.618016 22 6 B (0.27272727 0.72727273)
## 34) compactness_se>=-3.004445 7 2 M (0.71428571 0.28571429)
## 68) texture_mean>=3.038737 5 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 3.038737 2 0 B (0.00000000 1.00000000) *
## 35) compactness_se< -3.004445 15 1 B (0.06666667 0.93333333)
## 70) compactness_se< -4.171292 1 0 M (1.00000000 0.00000000) *
## 71) compactness_se>=-4.171292 14 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.275512 92 40 B (0.43478261 0.56521739)
## 18) smoothness_mean< -2.40097 51 17 M (0.66666667 0.33333333)
## 36) symmetry_worst>=-1.953246 37 5 M (0.86486486 0.13513514)
## 72) texture_mean< 3.192081 34 2 M (0.94117647 0.05882353) *
## 73) texture_mean>=3.192081 3 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.953246 14 2 B (0.14285714 0.85714286)
## 74) smoothness_worst>=-1.552639 2 0 M (1.00000000 0.00000000) *
## 75) smoothness_worst< -1.552639 12 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.40097 41 6 B (0.14634146 0.85365854)
## 38) smoothness_worst< -1.628375 2 0 M (1.00000000 0.00000000) *
## 39) smoothness_worst>=-1.628375 39 4 B (0.10256410 0.89743590)
## 78) smoothness_worst>=-1.433001 1 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.433001 38 3 B (0.07894737 0.92105263) *
## 5) smoothness_mean>=-2.106736 20 2 B (0.10000000 0.90000000)
## 10) smoothness_mean>=-2.05387 2 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean< -2.05387 18 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.960364 470 176 B (0.37446809 0.62553191)
## 6) symmetry_worst>=-1.348749 24 4 M (0.83333333 0.16666667)
## 12) compactness_se< -2.588521 22 2 M (0.90909091 0.09090909)
## 24) smoothness_mean>=-2.360133 21 1 M (0.95238095 0.04761905)
## 48) smoothness_mean< -2.022167 20 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.022167 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.360133 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-2.588521 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.348749 446 156 B (0.34977578 0.65022422)
## 14) texture_mean>=2.708379 400 153 B (0.38250000 0.61750000)
## 28) symmetry_worst< -1.707562 242 113 B (0.46694215 0.53305785)
## 56) texture_worst< 4.54138 175 78 M (0.55428571 0.44571429)
## 112) texture_worst>=4.507583 36 3 M (0.91666667 0.08333333) *
## 113) texture_worst< 4.507583 139 64 B (0.46043165 0.53956835) *
## 57) texture_worst>=4.54138 67 16 B (0.23880597 0.76119403)
## 114) smoothness_mean< -2.350326 27 11 M (0.59259259 0.40740741) *
## 115) smoothness_mean>=-2.350326 40 0 B (0.00000000 1.00000000) *
## 29) symmetry_worst>=-1.707562 158 40 B (0.25316456 0.74683544)
## 58) smoothness_mean>=-2.177741 21 3 M (0.85714286 0.14285714)
## 116) texture_worst>=4.148692 18 0 M (1.00000000 0.00000000) *
## 117) texture_worst< 4.148692 3 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.177741 137 22 B (0.16058394 0.83941606)
## 118) smoothness_worst>=-1.482509 50 17 B (0.34000000 0.66000000) *
## 119) smoothness_worst< -1.482509 87 5 B (0.05747126 0.94252874) *
## 15) texture_mean< 2.708379 46 3 B (0.06521739 0.93478261)
## 30) smoothness_mean>=-2.074653 7 3 B (0.42857143 0.57142857)
## 60) smoothness_mean< -2.060513 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.060513 4 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.074653 39 0 B (0.00000000 1.00000000) *
##
## $trees[[48]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 B (0.47149123 0.52850877)
## 2) texture_mean>=2.708379 870 425 B (0.48850575 0.51149425)
## 4) texture_worst< 4.467083 259 96 M (0.62934363 0.37065637)
## 8) smoothness_mean< -2.262885 205 60 M (0.70731707 0.29268293)
## 16) smoothness_worst>=-1.637109 193 48 M (0.75129534 0.24870466)
## 32) texture_worst>=4.254671 131 21 M (0.83969466 0.16030534)
## 64) symmetry_worst< -1.403642 127 17 M (0.86614173 0.13385827) *
## 65) symmetry_worst>=-1.403642 4 0 B (0.00000000 1.00000000) *
## 33) texture_worst< 4.254671 62 27 M (0.56451613 0.43548387)
## 66) texture_worst< 4.190306 49 14 M (0.71428571 0.28571429) *
## 67) texture_worst>=4.190306 13 0 B (0.00000000 1.00000000) *
## 17) smoothness_worst< -1.637109 12 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.262885 54 18 B (0.33333333 0.66666667)
## 18) smoothness_mean>=-2.175971 24 7 M (0.70833333 0.29166667)
## 36) compactness_se>=-3.668604 18 1 M (0.94444444 0.05555556)
## 72) texture_worst>=3.952268 17 0 M (1.00000000 0.00000000) *
## 73) texture_worst< 3.952268 1 0 B (0.00000000 1.00000000) *
## 37) compactness_se< -3.668604 6 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.175971 30 1 B (0.03333333 0.96666667)
## 38) texture_worst< 4.036973 1 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.036973 29 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.467083 611 262 B (0.42880524 0.57119476)
## 10) symmetry_worst>=-1.549706 115 45 M (0.60869565 0.39130435)
## 20) compactness_se< -3.180898 105 36 M (0.65714286 0.34285714)
## 40) texture_worst>=4.61159 75 17 M (0.77333333 0.22666667)
## 80) compactness_se>=-4.694501 72 14 M (0.80555556 0.19444444) *
## 81) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 41) texture_worst< 4.61159 30 11 B (0.36666667 0.63333333)
## 82) texture_mean>=2.950145 13 2 M (0.84615385 0.15384615) *
## 83) texture_mean< 2.950145 17 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-3.180898 10 1 B (0.10000000 0.90000000)
## 42) smoothness_mean>=-2.346429 1 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.346429 9 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.549706 496 192 B (0.38709677 0.61290323)
## 22) texture_mean>=2.929857 400 173 B (0.43250000 0.56750000)
## 44) symmetry_worst< -1.606972 355 167 B (0.47042254 0.52957746)
## 88) smoothness_worst< -1.438548 319 157 M (0.50783699 0.49216301) *
## 89) smoothness_worst>=-1.438548 36 5 B (0.13888889 0.86111111) *
## 45) symmetry_worst>=-1.606972 45 6 B (0.13333333 0.86666667)
## 90) texture_worst< 4.581608 4 0 M (1.00000000 0.00000000) *
## 91) texture_worst>=4.581608 41 2 B (0.04878049 0.95121951) *
## 23) texture_mean< 2.929857 96 19 B (0.19791667 0.80208333)
## 46) texture_worst< 4.545141 36 15 B (0.41666667 0.58333333)
## 92) texture_worst>=4.508732 16 1 M (0.93750000 0.06250000) *
## 93) texture_worst< 4.508732 20 0 B (0.00000000 1.00000000) *
## 47) texture_worst>=4.545141 60 4 B (0.06666667 0.93333333)
## 94) compactness_se< -4.391048 10 4 B (0.40000000 0.60000000) *
## 95) compactness_se>=-4.391048 50 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.708379 42 5 B (0.11904762 0.88095238)
## 6) symmetry_worst>=-1.552505 13 5 B (0.38461538 0.61538462)
## 12) texture_mean< 2.518783 4 0 M (1.00000000 0.00000000) *
## 13) texture_mean>=2.518783 9 1 B (0.11111111 0.88888889)
## 26) compactness_se>=-3.3026 1 0 M (1.00000000 0.00000000) *
## 27) compactness_se< -3.3026 8 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.552505 29 0 B (0.00000000 1.00000000) *
##
## $trees[[49]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 363 B (0.39802632 0.60197368)
## 2) smoothness_worst>=-1.501069 361 176 B (0.48753463 0.51246537)
## 4) smoothness_worst< -1.476605 126 46 M (0.63492063 0.36507937)
## 8) smoothness_worst>=-1.482699 47 5 M (0.89361702 0.10638298)
## 16) texture_worst>=4.136746 45 3 M (0.93333333 0.06666667)
## 32) smoothness_mean< -2.241789 36 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.241789 9 3 M (0.66666667 0.33333333)
## 66) texture_mean>=2.858739 6 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.858739 3 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.136746 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482699 79 38 B (0.48101266 0.51898734)
## 18) smoothness_worst< -1.49223 41 11 M (0.73170732 0.26829268)
## 36) symmetry_worst< -1.456355 37 7 M (0.81081081 0.18918919)
## 72) smoothness_mean>=-2.374383 33 4 M (0.87878788 0.12121212) *
## 73) smoothness_mean< -2.374383 4 1 B (0.25000000 0.75000000) *
## 37) symmetry_worst>=-1.456355 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.49223 38 8 B (0.21052632 0.78947368)
## 38) symmetry_worst>=-1.413975 3 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.413975 35 5 B (0.14285714 0.85714286)
## 78) texture_mean>=3.407548 2 0 M (1.00000000 0.00000000) *
## 79) texture_mean< 3.407548 33 3 B (0.09090909 0.90909091) *
## 5) smoothness_worst>=-1.476605 235 96 B (0.40851064 0.59148936)
## 10) smoothness_worst>=-1.473476 193 95 B (0.49222798 0.50777202)
## 20) smoothness_mean< -2.300091 52 13 M (0.75000000 0.25000000)
## 40) smoothness_mean>=-2.362601 33 2 M (0.93939394 0.06060606)
## 80) compactness_se>=-4.494315 31 0 M (1.00000000 0.00000000) *
## 81) compactness_se< -4.494315 2 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean< -2.362601 19 8 B (0.42105263 0.57894737)
## 82) symmetry_worst>=-1.446218 7 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst< -1.446218 12 1 B (0.08333333 0.91666667) *
## 21) smoothness_mean>=-2.300091 141 56 B (0.39716312 0.60283688)
## 42) compactness_se>=-4.02632 111 54 B (0.48648649 0.51351351)
## 84) smoothness_mean>=-2.288684 89 35 M (0.60674157 0.39325843) *
## 85) smoothness_mean< -2.288684 22 0 B (0.00000000 1.00000000) *
## 43) compactness_se< -4.02632 30 2 B (0.06666667 0.93333333)
## 86) symmetry_worst< -1.743442 5 2 B (0.40000000 0.60000000) *
## 87) symmetry_worst>=-1.743442 25 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473476 42 1 B (0.02380952 0.97619048)
## 22) texture_mean>=3.069079 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.069079 41 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.501069 551 187 B (0.33938294 0.66061706)
## 6) texture_worst< 4.467472 160 77 M (0.51875000 0.48125000)
## 12) compactness_se< -3.48221 132 51 M (0.61363636 0.38636364)
## 24) texture_worst>=3.891616 118 37 M (0.68644068 0.31355932)
## 48) compactness_se>=-4.519704 102 23 M (0.77450980 0.22549020)
## 96) symmetry_worst< -1.576447 95 16 M (0.83157895 0.16842105) *
## 97) symmetry_worst>=-1.576447 7 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.519704 16 2 B (0.12500000 0.87500000)
## 98) smoothness_mean>=-2.306694 2 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean< -2.306694 14 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.891616 14 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.48221 28 2 B (0.07142857 0.92857143)
## 26) texture_worst>=4.411908 2 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.411908 26 0 B (0.00000000 1.00000000) *
## 7) texture_worst>=4.467472 391 104 B (0.26598465 0.73401535)
## 14) compactness_se>=-3.005655 20 6 M (0.70000000 0.30000000)
## 28) texture_mean>=3.058386 14 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 3.058386 6 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.005655 371 90 B (0.24258760 0.75741240)
## 30) compactness_se< -4.032373 140 55 B (0.39285714 0.60714286)
## 60) texture_worst>=5.149489 19 3 M (0.84210526 0.15789474)
## 120) symmetry_worst>=-2.036673 16 0 M (1.00000000 0.00000000) *
## 121) symmetry_worst< -2.036673 3 0 B (0.00000000 1.00000000) *
## 61) texture_worst< 5.149489 121 39 B (0.32231405 0.67768595)
## 122) smoothness_worst< -1.607486 21 6 M (0.71428571 0.28571429) *
## 123) smoothness_worst>=-1.607486 100 24 B (0.24000000 0.76000000) *
## 31) compactness_se>=-4.032373 231 35 B (0.15151515 0.84848485)
## 62) symmetry_worst>=-1.789477 91 24 B (0.26373626 0.73626374)
## 124) symmetry_worst< -1.767566 8 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.767566 83 16 B (0.19277108 0.80722892) *
## 63) symmetry_worst< -1.789477 140 11 B (0.07857143 0.92142857)
## 126) texture_mean>=3.313386 9 4 M (0.55555556 0.44444444) *
## 127) texture_mean< 3.313386 131 6 B (0.04580153 0.95419847) *
##
## $trees[[50]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 372 B (0.40789474 0.59210526)
## 2) symmetry_worst>=-1.775265 446 215 M (0.51793722 0.48206278)
## 4) texture_mean>=2.920739 316 125 M (0.60443038 0.39556962)
## 8) texture_worst< 4.930927 249 82 M (0.67068273 0.32931727)
## 16) compactness_se< -4.07887 78 9 M (0.88461538 0.11538462)
## 32) symmetry_worst< -1.51291 64 2 M (0.96875000 0.03125000)
## 64) smoothness_mean< -2.306529 59 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.306529 5 2 M (0.60000000 0.40000000) *
## 33) symmetry_worst>=-1.51291 14 7 M (0.50000000 0.50000000)
## 66) texture_mean>=2.99247 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.99247 7 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-4.07887 171 73 M (0.57309942 0.42690058)
## 34) compactness_se>=-3.681558 102 26 M (0.74509804 0.25490196)
## 68) compactness_se< -3.502612 41 0 M (1.00000000 0.00000000) *
## 69) compactness_se>=-3.502612 61 26 M (0.57377049 0.42622951) *
## 35) compactness_se< -3.681558 69 22 B (0.31884058 0.68115942)
## 70) smoothness_worst>=-1.462341 24 6 M (0.75000000 0.25000000) *
## 71) smoothness_worst< -1.462341 45 4 B (0.08888889 0.91111111) *
## 9) texture_worst>=4.930927 67 24 B (0.35820896 0.64179104)
## 18) texture_worst>=5.003123 33 11 M (0.66666667 0.33333333)
## 36) texture_mean< 3.225651 14 0 M (1.00000000 0.00000000) *
## 37) texture_mean>=3.225651 19 8 B (0.42105263 0.57894737)
## 74) smoothness_mean>=-2.329526 5 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean< -2.329526 14 3 B (0.21428571 0.78571429) *
## 19) texture_worst< 5.003123 34 2 B (0.05882353 0.94117647)
## 38) symmetry_worst>=-1.450896 1 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.450896 33 1 B (0.03030303 0.96969697)
## 78) texture_mean< 3.088538 4 1 B (0.25000000 0.75000000) *
## 79) texture_mean>=3.088538 29 0 B (0.00000000 1.00000000) *
## 5) texture_mean< 2.920739 130 40 B (0.30769231 0.69230769)
## 10) texture_mean< 2.850705 92 39 B (0.42391304 0.57608696)
## 20) texture_mean>=2.824054 26 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 2.824054 66 13 B (0.19696970 0.80303030)
## 42) smoothness_worst>=-1.491834 34 13 B (0.38235294 0.61764706)
## 84) texture_worst>=4.110502 14 5 M (0.64285714 0.35714286) *
## 85) texture_worst< 4.110502 20 4 B (0.20000000 0.80000000) *
## 43) smoothness_worst< -1.491834 32 0 B (0.00000000 1.00000000) *
## 11) texture_mean>=2.850705 38 1 B (0.02631579 0.97368421)
## 22) smoothness_mean>=-2.213964 1 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean< -2.213964 37 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.775265 466 141 B (0.30257511 0.69742489)
## 6) smoothness_worst>=-1.603315 406 138 B (0.33990148 0.66009852)
## 12) smoothness_worst< -1.602623 10 0 M (1.00000000 0.00000000) *
## 13) smoothness_worst>=-1.602623 396 128 B (0.32323232 0.67676768)
## 26) compactness_se< -3.869459 175 75 B (0.42857143 0.57142857)
## 52) texture_worst>=4.907333 33 6 M (0.81818182 0.18181818)
## 104) texture_mean< 3.353705 30 3 M (0.90000000 0.10000000) *
## 105) texture_mean>=3.353705 3 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.907333 142 48 B (0.33802817 0.66197183)
## 106) texture_worst< 4.751011 109 48 B (0.44036697 0.55963303) *
## 107) texture_worst>=4.751011 33 0 B (0.00000000 1.00000000) *
## 27) compactness_se>=-3.869459 221 53 B (0.23981900 0.76018100)
## 54) smoothness_mean< -2.473552 12 1 M (0.91666667 0.08333333)
## 108) smoothness_mean>=-2.497829 11 0 M (1.00000000 0.00000000) *
## 109) smoothness_mean< -2.497829 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_mean>=-2.473552 209 42 B (0.20095694 0.79904306)
## 110) smoothness_mean>=-2.377849 129 42 B (0.32558140 0.67441860) *
## 111) smoothness_mean< -2.377849 80 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.603315 60 3 B (0.05000000 0.95000000)
## 14) compactness_se>=-2.951614 1 0 M (1.00000000 0.00000000) *
## 15) compactness_se< -2.951614 59 2 B (0.03389831 0.96610169)
## 30) compactness_se< -4.691681 6 2 B (0.33333333 0.66666667)
## 60) compactness_se>=-4.711555 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -4.711555 4 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-4.691681 53 0 B (0.00000000 1.00000000) *
##
## $trees[[51]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 388 B (0.42543860 0.57456140)
## 2) texture_mean>=2.853016 706 326 B (0.46175637 0.53824363)
## 4) smoothness_worst>=-1.556752 499 243 M (0.51302605 0.48697395)
## 8) smoothness_mean< -2.349264 199 67 M (0.66331658 0.33668342)
## 16) compactness_se< -3.938851 115 20 M (0.82608696 0.17391304)
## 32) smoothness_mean>=-2.473387 103 8 M (0.92233010 0.07766990)
## 64) symmetry_worst>=-1.959872 83 2 M (0.97590361 0.02409639) *
## 65) symmetry_worst< -1.959872 20 6 M (0.70000000 0.30000000) *
## 33) smoothness_mean< -2.473387 12 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.938851 84 37 B (0.44047619 0.55952381)
## 34) smoothness_mean>=-2.362071 15 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean< -2.362071 69 22 B (0.31884058 0.68115942)
## 70) smoothness_mean< -2.461054 12 0 M (1.00000000 0.00000000) *
## 71) smoothness_mean>=-2.461054 57 10 B (0.17543860 0.82456140) *
## 9) smoothness_mean>=-2.349264 300 124 B (0.41333333 0.58666667)
## 18) compactness_se>=-3.219881 34 8 M (0.76470588 0.23529412)
## 36) smoothness_mean>=-2.332581 30 4 M (0.86666667 0.13333333)
## 72) smoothness_mean< -2.224699 21 0 M (1.00000000 0.00000000) *
## 73) smoothness_mean>=-2.224699 9 4 M (0.55555556 0.44444444) *
## 37) smoothness_mean< -2.332581 4 0 B (0.00000000 1.00000000) *
## 19) compactness_se< -3.219881 266 98 B (0.36842105 0.63157895)
## 38) texture_mean< 3.039744 166 76 B (0.45783133 0.54216867)
## 76) compactness_se>=-3.669769 54 15 M (0.72222222 0.27777778) *
## 77) compactness_se< -3.669769 112 37 B (0.33035714 0.66964286) *
## 39) texture_mean>=3.039744 100 22 B (0.22000000 0.78000000)
## 78) texture_worst< 4.664833 5 0 M (1.00000000 0.00000000) *
## 79) texture_worst>=4.664833 95 17 B (0.17894737 0.82105263) *
## 5) smoothness_worst< -1.556752 207 70 B (0.33816425 0.66183575)
## 10) symmetry_worst< -2.242382 22 3 M (0.86363636 0.13636364)
## 20) smoothness_worst>=-1.645584 16 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.645584 6 3 M (0.50000000 0.50000000)
## 42) texture_mean>=3.175045 3 0 M (1.00000000 0.00000000) *
## 43) texture_mean< 3.175045 3 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-2.242382 185 51 B (0.27567568 0.72432432)
## 22) smoothness_worst< -1.568787 127 45 B (0.35433071 0.64566929)
## 44) smoothness_worst>=-1.584838 24 5 M (0.79166667 0.20833333)
## 88) texture_mean>=2.924461 19 0 M (1.00000000 0.00000000) *
## 89) texture_mean< 2.924461 5 0 B (0.00000000 1.00000000) *
## 45) smoothness_worst< -1.584838 103 26 B (0.25242718 0.74757282)
## 90) symmetry_worst>=-1.795801 43 21 B (0.48837209 0.51162791) *
## 91) symmetry_worst< -1.795801 60 5 B (0.08333333 0.91666667) *
## 23) smoothness_worst>=-1.568787 58 6 B (0.10344828 0.89655172)
## 46) smoothness_mean>=-2.299648 2 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.299648 56 4 B (0.07142857 0.92857143)
## 94) compactness_se>=-2.682598 2 0 M (1.00000000 0.00000000) *
## 95) compactness_se< -2.682598 54 2 B (0.03703704 0.96296296) *
## 3) texture_mean< 2.853016 206 62 B (0.30097087 0.69902913)
## 6) smoothness_worst< -1.468426 135 54 B (0.40000000 0.60000000)
## 12) smoothness_mean>=-2.443746 112 54 B (0.48214286 0.51785714)
## 24) texture_mean>=2.656405 99 45 M (0.54545455 0.45454545)
## 48) texture_mean< 2.744378 30 3 M (0.90000000 0.10000000)
## 96) smoothness_mean< -2.205363 28 1 M (0.96428571 0.03571429) *
## 97) smoothness_mean>=-2.205363 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean>=2.744378 69 27 B (0.39130435 0.60869565)
## 98) smoothness_mean< -2.437331 7 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.437331 62 20 B (0.32258065 0.67741935) *
## 25) texture_mean< 2.656405 13 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.443746 23 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.468426 71 8 B (0.11267606 0.88732394)
## 14) texture_worst>=4.398698 3 0 M (1.00000000 0.00000000) *
## 15) texture_worst< 4.398698 68 5 B (0.07352941 0.92647059)
## 30) symmetry_worst>=-1.619683 32 5 B (0.15625000 0.84375000)
## 60) symmetry_worst< -1.483416 6 2 M (0.66666667 0.33333333)
## 120) compactness_se>=-3.961508 4 0 M (1.00000000 0.00000000) *
## 121) compactness_se< -3.961508 2 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst>=-1.483416 26 1 B (0.03846154 0.96153846)
## 122) smoothness_worst< -1.454202 1 0 M (1.00000000 0.00000000) *
## 123) smoothness_worst>=-1.454202 25 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.619683 36 0 B (0.00000000 1.00000000) *
##
## $trees[[52]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 M (0.50877193 0.49122807)
## 2) compactness_se< -3.867535 482 199 M (0.58713693 0.41286307)
## 4) texture_mean>=2.754924 447 164 M (0.63310962 0.36689038)
## 8) compactness_se>=-3.883925 38 0 M (1.00000000 0.00000000) *
## 9) compactness_se< -3.883925 409 164 M (0.59902200 0.40097800)
## 18) symmetry_worst>=-1.966052 318 105 M (0.66981132 0.33018868)
## 36) compactness_se< -3.902076 303 90 M (0.70297030 0.29702970)
## 72) smoothness_mean< -2.300091 240 56 M (0.76666667 0.23333333) *
## 73) smoothness_mean>=-2.300091 63 29 B (0.46031746 0.53968254) *
## 37) compactness_se>=-3.902076 15 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.966052 91 32 B (0.35164835 0.64835165)
## 38) symmetry_worst< -2.170754 31 10 M (0.67741935 0.32258065)
## 76) smoothness_mean>=-2.392268 24 3 M (0.87500000 0.12500000) *
## 77) smoothness_mean< -2.392268 7 0 B (0.00000000 1.00000000) *
## 39) symmetry_worst>=-2.170754 60 11 B (0.18333333 0.81666667)
## 78) smoothness_worst>=-1.525709 15 5 M (0.66666667 0.33333333) *
## 79) smoothness_worst< -1.525709 45 1 B (0.02222222 0.97777778) *
## 5) texture_mean< 2.754924 35 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-3.867535 430 181 B (0.42093023 0.57906977)
## 6) compactness_se>=-3.721197 345 165 B (0.47826087 0.52173913)
## 12) compactness_se< -3.575734 77 21 M (0.72727273 0.27272727)
## 24) smoothness_mean>=-2.423737 53 6 M (0.88679245 0.11320755)
## 48) texture_mean>=2.647471 50 3 M (0.94000000 0.06000000)
## 96) symmetry_worst>=-2.174989 44 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst< -2.174989 6 3 M (0.50000000 0.50000000) *
## 49) texture_mean< 2.647471 3 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.423737 24 9 B (0.37500000 0.62500000)
## 50) symmetry_worst>=-1.813961 10 1 M (0.90000000 0.10000000)
## 100) smoothness_mean< -2.452956 9 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean>=-2.452956 1 0 B (0.00000000 1.00000000) *
## 51) symmetry_worst< -1.813961 14 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.575734 268 109 B (0.40671642 0.59328358)
## 26) texture_mean>=3.059388 81 29 M (0.64197531 0.35802469)
## 52) texture_worst< 4.745147 17 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=4.745147 64 29 M (0.54687500 0.45312500)
## 106) texture_worst>=5.016194 23 5 M (0.78260870 0.21739130) *
## 107) texture_worst< 5.016194 41 17 B (0.41463415 0.58536585) *
## 27) texture_mean< 3.059388 187 57 B (0.30481283 0.69518717)
## 54) smoothness_worst>=-1.502084 87 43 M (0.50574713 0.49425287)
## 108) smoothness_worst< -1.476605 30 5 M (0.83333333 0.16666667) *
## 109) smoothness_worst>=-1.476605 57 19 B (0.33333333 0.66666667) *
## 55) smoothness_worst< -1.502084 100 13 B (0.13000000 0.87000000)
## 110) texture_mean< 2.782752 15 6 M (0.60000000 0.40000000) *
## 111) texture_mean>=2.782752 85 4 B (0.04705882 0.95294118) *
## 7) compactness_se< -3.721197 85 16 B (0.18823529 0.81176471)
## 14) smoothness_worst>=-1.48132 27 11 M (0.59259259 0.40740741)
## 28) texture_mean>=2.971675 12 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.971675 15 4 B (0.26666667 0.73333333)
## 58) symmetry_worst>=-1.612049 4 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.612049 11 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.48132 58 0 B (0.00000000 1.00000000) *
##
## $trees[[53]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) smoothness_worst>=-1.559144 682 299 M (0.56158358 0.43841642)
## 4) symmetry_worst>=-2.233349 648 269 M (0.58487654 0.41512346)
## 8) smoothness_mean< -2.26529 479 174 M (0.63674322 0.36325678)
## 16) smoothness_mean>=-2.416986 388 125 M (0.67783505 0.32216495)
## 32) symmetry_worst>=-2.207519 379 116 M (0.69393140 0.30606860)
## 64) texture_mean< 3.36829 370 108 M (0.70810811 0.29189189) *
## 65) texture_mean>=3.36829 9 1 B (0.11111111 0.88888889) *
## 33) symmetry_worst< -2.207519 9 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.416986 91 42 B (0.46153846 0.53846154)
## 34) smoothness_worst< -1.551775 35 7 M (0.80000000 0.20000000)
## 68) texture_mean>=2.859755 31 3 M (0.90322581 0.09677419) *
## 69) texture_mean< 2.859755 4 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.551775 56 14 B (0.25000000 0.75000000)
## 70) texture_mean>=3.111958 22 8 M (0.63636364 0.36363636) *
## 71) texture_mean< 3.111958 34 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.26529 169 74 B (0.43786982 0.56213018)
## 18) smoothness_mean>=-2.222851 92 38 M (0.58695652 0.41304348)
## 36) smoothness_mean< -2.093138 73 23 M (0.68493151 0.31506849)
## 72) compactness_se>=-3.673673 31 1 M (0.96774194 0.03225806) *
## 73) compactness_se< -3.673673 42 20 B (0.47619048 0.52380952) *
## 37) smoothness_mean>=-2.093138 19 4 B (0.21052632 0.78947368)
## 74) symmetry_worst>=-1.596878 7 3 M (0.57142857 0.42857143) *
## 75) symmetry_worst< -1.596878 12 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.222851 77 20 B (0.25974026 0.74025974)
## 38) smoothness_mean< -2.235394 41 18 B (0.43902439 0.56097561)
## 76) smoothness_worst>=-1.45841 21 5 M (0.76190476 0.23809524) *
## 77) smoothness_worst< -1.45841 20 2 B (0.10000000 0.90000000) *
## 39) smoothness_mean>=-2.235394 36 2 B (0.05555556 0.94444444)
## 78) texture_mean< 2.693961 2 0 M (1.00000000 0.00000000) *
## 79) texture_mean>=2.693961 34 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -2.233349 34 4 B (0.11764706 0.88235294)
## 10) compactness_se>=-2.833542 4 0 M (1.00000000 0.00000000) *
## 11) compactness_se< -2.833542 30 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.559144 230 87 B (0.37826087 0.62173913)
## 6) smoothness_worst< -1.565486 196 86 B (0.43877551 0.56122449)
## 12) texture_worst>=4.354728 158 79 M (0.50000000 0.50000000)
## 24) symmetry_worst>=-1.720706 56 17 M (0.69642857 0.30357143)
## 48) texture_mean>=2.958609 48 9 M (0.81250000 0.18750000)
## 96) smoothness_worst>=-1.660611 45 6 M (0.86666667 0.13333333) *
## 97) smoothness_worst< -1.660611 3 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.958609 8 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.720706 102 40 B (0.39215686 0.60784314)
## 50) compactness_se>=-4.104699 50 20 M (0.60000000 0.40000000)
## 100) texture_worst>=4.56463 33 5 M (0.84848485 0.15151515) *
## 101) texture_worst< 4.56463 17 2 B (0.11764706 0.88235294) *
## 51) compactness_se< -4.104699 52 10 B (0.19230769 0.80769231)
## 102) symmetry_worst< -2.382417 8 0 M (1.00000000 0.00000000) *
## 103) symmetry_worst>=-2.382417 44 2 B (0.04545455 0.95454545) *
## 13) texture_worst< 4.354728 38 7 B (0.18421053 0.81578947)
## 26) smoothness_mean>=-2.30797 5 0 M (1.00000000 0.00000000) *
## 27) smoothness_mean< -2.30797 33 2 B (0.06060606 0.93939394)
## 54) texture_worst< 3.948691 5 2 B (0.40000000 0.60000000)
## 108) texture_mean>=2.754513 2 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.754513 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=3.948691 28 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.565486 34 1 B (0.02941176 0.97058824)
## 14) smoothness_mean>=-2.299648 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_mean< -2.299648 33 0 B (0.00000000 1.00000000) *
##
## $trees[[54]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 389 B (0.42653509 0.57346491)
## 2) texture_worst>=4.275472 772 357 B (0.46243523 0.53756477)
## 4) symmetry_worst>=-2.041024 641 313 M (0.51170047 0.48829953)
## 8) smoothness_mean>=-2.21595 79 19 M (0.75949367 0.24050633)
## 16) symmetry_worst>=-1.766269 52 3 M (0.94230769 0.05769231)
## 32) texture_mean< 3.039982 42 0 M (1.00000000 0.00000000) *
## 33) texture_mean>=3.039982 10 3 M (0.70000000 0.30000000)
## 66) texture_mean>=3.045947 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.045947 3 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.766269 27 11 B (0.40740741 0.59259259)
## 34) symmetry_worst< -1.891461 10 1 M (0.90000000 0.10000000)
## 68) texture_mean>=2.967292 9 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.967292 1 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst>=-1.891461 17 2 B (0.11764706 0.88235294)
## 70) compactness_se>=-3.317826 2 0 M (1.00000000 0.00000000) *
## 71) compactness_se< -3.317826 15 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.21595 562 268 B (0.47686833 0.52313167)
## 18) texture_worst< 4.280533 24 2 M (0.91666667 0.08333333)
## 36) smoothness_mean>=-2.473491 22 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean< -2.473491 2 0 B (0.00000000 1.00000000) *
## 19) texture_worst>=4.280533 538 246 B (0.45724907 0.54275093)
## 38) texture_worst>=4.517889 412 201 M (0.51213592 0.48786408)
## 76) texture_worst< 4.644679 148 49 M (0.66891892 0.33108108) *
## 77) texture_worst>=4.644679 264 112 B (0.42424242 0.57575758) *
## 39) texture_worst< 4.517889 126 35 B (0.27777778 0.72222222)
## 78) texture_mean>=2.960623 27 5 M (0.81481481 0.18518519) *
## 79) texture_mean< 2.960623 99 13 B (0.13131313 0.86868687) *
## 5) symmetry_worst< -2.041024 131 29 B (0.22137405 0.77862595)
## 10) symmetry_worst< -2.379234 15 4 M (0.73333333 0.26666667)
## 20) texture_mean< 3.283931 12 1 M (0.91666667 0.08333333)
## 40) smoothness_mean< -2.287736 10 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean>=-2.287736 2 1 M (0.50000000 0.50000000)
## 82) texture_mean>=3.050671 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.050671 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=3.283931 3 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-2.379234 116 18 B (0.15517241 0.84482759)
## 22) compactness_se>=-2.72933 5 1 M (0.80000000 0.20000000)
## 44) texture_mean>=3.063909 4 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.063909 1 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -2.72933 111 14 B (0.12612613 0.87387387)
## 46) smoothness_worst< -1.709211 2 0 M (1.00000000 0.00000000) *
## 47) smoothness_worst>=-1.709211 109 12 B (0.11009174 0.88990826)
## 94) smoothness_worst>=-1.448989 1 0 M (1.00000000 0.00000000) *
## 95) smoothness_worst< -1.448989 108 11 B (0.10185185 0.89814815) *
## 3) texture_worst< 4.275472 140 32 B (0.22857143 0.77142857)
## 6) texture_mean>=2.757473 65 23 B (0.35384615 0.64615385)
## 12) texture_worst< 4.012259 10 0 M (1.00000000 0.00000000) *
## 13) texture_worst>=4.012259 55 13 B (0.23636364 0.76363636)
## 26) texture_mean< 2.760642 7 0 M (1.00000000 0.00000000) *
## 27) texture_mean>=2.760642 48 6 B (0.12500000 0.87500000)
## 54) symmetry_worst>=-1.431518 8 2 M (0.75000000 0.25000000)
## 108) texture_mean< 2.914443 6 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.914443 2 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.431518 40 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.757473 75 9 B (0.12000000 0.88000000)
## 14) texture_mean< 2.715026 38 9 B (0.23684211 0.76315789)
## 28) texture_mean>=2.709047 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.709047 34 5 B (0.14705882 0.85294118)
## 58) texture_mean< 2.515298 6 3 M (0.50000000 0.50000000)
## 116) smoothness_mean< -2.060513 3 0 M (1.00000000 0.00000000) *
## 117) smoothness_mean>=-2.060513 3 0 B (0.00000000 1.00000000) *
## 59) texture_mean>=2.515298 28 2 B (0.07142857 0.92857143)
## 118) smoothness_mean< -2.298096 6 2 B (0.33333333 0.66666667) *
## 119) smoothness_mean>=-2.298096 22 0 B (0.00000000 1.00000000) *
## 15) texture_mean>=2.715026 37 0 B (0.00000000 1.00000000) *
##
## $trees[[55]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) texture_worst>=4.580648 486 191 M (0.60699588 0.39300412)
## 4) smoothness_mean>=-2.408446 368 116 M (0.68478261 0.31521739)
## 8) texture_worst>=4.895983 161 31 M (0.80745342 0.19254658)
## 16) smoothness_mean>=-2.336091 120 10 M (0.91666667 0.08333333)
## 32) smoothness_mean< -2.106736 115 6 M (0.94782609 0.05217391)
## 64) compactness_se>=-4.032549 109 2 M (0.98165138 0.01834862) *
## 65) compactness_se< -4.032549 6 2 B (0.33333333 0.66666667) *
## 33) smoothness_mean>=-2.106736 5 1 B (0.20000000 0.80000000)
## 66) texture_mean< 3.181902 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.181902 4 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.336091 41 20 B (0.48780488 0.51219512)
## 34) smoothness_worst< -1.530302 14 1 M (0.92857143 0.07142857)
## 68) texture_mean< 3.392124 13 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.392124 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst>=-1.530302 27 7 B (0.25925926 0.74074074)
## 70) texture_worst< 5.113166 6 0 M (1.00000000 0.00000000) *
## 71) texture_worst>=5.113166 21 1 B (0.04761905 0.95238095) *
## 9) texture_worst< 4.895983 207 85 M (0.58937198 0.41062802)
## 18) smoothness_mean< -2.352368 82 10 M (0.87804878 0.12195122)
## 36) texture_worst< 4.876647 76 4 M (0.94736842 0.05263158)
## 72) texture_mean< 3.135225 71 0 M (1.00000000 0.00000000) *
## 73) texture_mean>=3.135225 5 1 B (0.20000000 0.80000000) *
## 37) texture_worst>=4.876647 6 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean>=-2.352368 125 50 B (0.40000000 0.60000000)
## 38) texture_worst< 4.608306 17 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.608306 108 33 B (0.30555556 0.69444444)
## 78) symmetry_worst>=-1.550826 39 14 M (0.64102564 0.35897436) *
## 79) symmetry_worst< -1.550826 69 8 B (0.11594203 0.88405797) *
## 5) smoothness_mean< -2.408446 118 43 B (0.36440678 0.63559322)
## 10) smoothness_mean< -2.443746 85 39 B (0.45882353 0.54117647)
## 20) smoothness_mean>=-2.489159 45 15 M (0.66666667 0.33333333)
## 40) texture_mean>=2.991714 34 7 M (0.79411765 0.20588235)
## 80) texture_worst< 5.316369 27 2 M (0.92592593 0.07407407) *
## 81) texture_worst>=5.316369 7 2 B (0.28571429 0.71428571) *
## 41) texture_mean< 2.991714 11 3 B (0.27272727 0.72727273)
## 82) smoothness_mean>=-2.457256 3 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.457256 8 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.489159 40 9 B (0.22500000 0.77500000)
## 42) texture_mean< 2.966301 5 0 M (1.00000000 0.00000000) *
## 43) texture_mean>=2.966301 35 4 B (0.11428571 0.88571429)
## 86) symmetry_worst>=-1.695215 11 4 B (0.36363636 0.63636364) *
## 87) symmetry_worst< -1.695215 24 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.443746 33 4 B (0.12121212 0.87878788)
## 22) texture_worst< 4.592462 3 0 M (1.00000000 0.00000000) *
## 23) texture_worst>=4.592462 30 1 B (0.03333333 0.96666667)
## 46) texture_worst>=5.149193 4 1 B (0.25000000 0.75000000)
## 92) texture_mean>=3.032025 1 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 3.032025 3 0 B (0.00000000 1.00000000) *
## 47) texture_worst< 5.149193 26 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.580648 426 153 B (0.35915493 0.64084507)
## 6) smoothness_worst>=-1.496637 178 87 B (0.48876404 0.51123596)
## 12) smoothness_worst< -1.476801 56 11 M (0.80357143 0.19642857)
## 24) smoothness_mean>=-2.315133 51 6 M (0.88235294 0.11764706)
## 48) smoothness_mean< -2.275944 38 0 M (1.00000000 0.00000000) *
## 49) smoothness_mean>=-2.275944 13 6 M (0.53846154 0.46153846)
## 98) texture_mean>=3.003189 7 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.003189 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.315133 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.476801 122 42 B (0.34426230 0.65573770)
## 26) smoothness_worst>=-1.451541 54 21 M (0.61111111 0.38888889)
## 52) texture_mean>=2.798684 30 4 M (0.86666667 0.13333333)
## 104) smoothness_mean< -2.155998 24 1 M (0.95833333 0.04166667) *
## 105) smoothness_mean>=-2.155998 6 3 M (0.50000000 0.50000000) *
## 53) texture_mean< 2.798684 24 7 B (0.29166667 0.70833333)
## 106) symmetry_worst>=-1.232339 3 0 M (1.00000000 0.00000000) *
## 107) symmetry_worst< -1.232339 21 4 B (0.19047619 0.80952381) *
## 27) smoothness_worst< -1.451541 68 9 B (0.13235294 0.86764706)
## 54) compactness_se>=-3.453499 12 4 M (0.66666667 0.33333333)
## 108) texture_mean< 2.801271 8 0 M (1.00000000 0.00000000) *
## 109) texture_mean>=2.801271 4 0 B (0.00000000 1.00000000) *
## 55) compactness_se< -3.453499 56 1 B (0.01785714 0.98214286)
## 110) texture_mean>=2.932513 1 0 M (1.00000000 0.00000000) *
## 111) texture_mean< 2.932513 55 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.496637 248 66 B (0.26612903 0.73387097)
## 14) symmetry_worst>=-1.830253 146 56 B (0.38356164 0.61643836)
## 28) symmetry_worst< -1.598517 113 55 B (0.48672566 0.51327434)
## 56) compactness_se< -3.93685 48 12 M (0.75000000 0.25000000)
## 112) smoothness_mean< -2.320044 43 7 M (0.83720930 0.16279070) *
## 113) smoothness_mean>=-2.320044 5 0 B (0.00000000 1.00000000) *
## 57) compactness_se>=-3.93685 65 19 B (0.29230769 0.70769231)
## 114) smoothness_mean>=-2.384488 23 7 M (0.69565217 0.30434783) *
## 115) smoothness_mean< -2.384488 42 3 B (0.07142857 0.92857143) *
## 29) symmetry_worst>=-1.598517 33 1 B (0.03030303 0.96969697)
## 58) texture_mean>=2.990556 1 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 2.990556 32 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.830253 102 10 B (0.09803922 0.90196078)
## 30) symmetry_worst< -2.880164 4 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst>=-2.880164 98 6 B (0.06122449 0.93877551)
## 62) compactness_se>=-2.988951 2 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -2.988951 96 4 B (0.04166667 0.95833333)
## 126) compactness_se< -4.49319 11 4 B (0.36363636 0.63636364) *
## 127) compactness_se>=-4.49319 85 0 B (0.00000000 1.00000000) *
##
## $trees[[56]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 429 B (0.47039474 0.52960526)
## 2) smoothness_worst>=-1.504307 393 171 M (0.56488550 0.43511450)
## 4) smoothness_worst< -1.476801 130 30 M (0.76923077 0.23076923)
## 8) compactness_se>=-4.245893 107 14 M (0.86915888 0.13084112)
## 16) smoothness_mean>=-2.367658 97 8 M (0.91752577 0.08247423)
## 32) symmetry_worst< -1.561818 77 2 M (0.97402597 0.02597403)
## 64) texture_mean>=2.647471 76 1 M (0.98684211 0.01315789) *
## 65) texture_mean< 2.647471 1 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst>=-1.561818 20 6 M (0.70000000 0.30000000)
## 66) smoothness_worst>=-1.491834 11 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst< -1.491834 9 3 B (0.33333333 0.66666667) *
## 17) smoothness_mean< -2.367658 10 4 B (0.40000000 0.60000000)
## 34) smoothness_mean< -2.408063 5 1 M (0.80000000 0.20000000)
## 68) texture_mean>=2.903025 4 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.903025 1 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.408063 5 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -4.245893 23 7 B (0.30434783 0.69565217)
## 18) symmetry_worst< -1.688448 9 2 M (0.77777778 0.22222222)
## 36) texture_mean>=2.800736 7 0 M (1.00000000 0.00000000) *
## 37) texture_mean< 2.800736 2 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst>=-1.688448 14 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.476801 263 122 B (0.46387833 0.53612167)
## 10) smoothness_mean>=-2.225626 67 17 M (0.74626866 0.25373134)
## 20) symmetry_worst>=-1.766269 52 6 M (0.88461538 0.11538462)
## 40) smoothness_worst< -1.396673 36 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst>=-1.396673 16 6 M (0.62500000 0.37500000)
## 82) symmetry_worst>=-1.616162 10 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst< -1.616162 6 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.766269 15 4 B (0.26666667 0.73333333)
## 42) smoothness_worst< -1.465711 4 0 M (1.00000000 0.00000000) *
## 43) smoothness_worst>=-1.465711 11 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.225626 196 72 B (0.36734694 0.63265306)
## 22) texture_worst>=4.866447 58 19 M (0.67241379 0.32758621)
## 44) compactness_se>=-4.512898 47 8 M (0.82978723 0.17021277)
## 88) compactness_se< -2.942351 43 4 M (0.90697674 0.09302326) *
## 89) compactness_se>=-2.942351 4 0 B (0.00000000 1.00000000) *
## 45) compactness_se< -4.512898 11 0 B (0.00000000 1.00000000) *
## 23) texture_worst< 4.866447 138 33 B (0.23913043 0.76086957)
## 46) symmetry_worst>=-1.864441 104 33 B (0.31730769 0.68269231)
## 92) texture_worst< 4.786713 78 32 B (0.41025641 0.58974359) *
## 93) texture_worst>=4.786713 26 1 B (0.03846154 0.96153846) *
## 47) symmetry_worst< -1.864441 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.504307 519 207 B (0.39884393 0.60115607)
## 6) smoothness_worst< -1.533657 365 172 B (0.47123288 0.52876712)
## 12) smoothness_worst>=-1.556752 113 42 M (0.62831858 0.37168142)
## 24) compactness_se< -4.087687 51 8 M (0.84313725 0.15686275)
## 48) smoothness_mean>=-2.469882 46 3 M (0.93478261 0.06521739)
## 96) smoothness_worst< -1.539367 43 0 M (1.00000000 0.00000000) *
## 97) smoothness_worst>=-1.539367 3 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean< -2.469882 5 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-4.087687 62 28 B (0.45161290 0.54838710)
## 50) compactness_se>=-3.569872 30 4 M (0.86666667 0.13333333)
## 100) symmetry_worst< -1.710027 23 0 M (1.00000000 0.00000000) *
## 101) symmetry_worst>=-1.710027 7 3 B (0.42857143 0.57142857) *
## 51) compactness_se< -3.569872 32 2 B (0.06250000 0.93750000)
## 102) symmetry_worst>=-1.617937 3 1 M (0.66666667 0.33333333) *
## 103) symmetry_worst< -1.617937 29 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.556752 252 101 B (0.40079365 0.59920635)
## 26) smoothness_worst< -1.563512 219 100 B (0.45662100 0.54337900)
## 52) texture_worst< 3.981173 11 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=3.981173 208 89 B (0.42788462 0.57211538)
## 106) texture_worst>=4.609399 111 49 M (0.55855856 0.44144144) *
## 107) texture_worst< 4.609399 97 27 B (0.27835052 0.72164948) *
## 27) smoothness_worst>=-1.563512 33 1 B (0.03030303 0.96969697)
## 54) texture_mean>=3.274729 1 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 3.274729 32 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst>=-1.533657 154 35 B (0.22727273 0.77272727)
## 14) smoothness_mean< -2.329341 69 31 B (0.44927536 0.55072464)
## 28) smoothness_mean>=-2.369786 31 8 M (0.74193548 0.25806452)
## 56) compactness_se>=-3.979062 27 4 M (0.85185185 0.14814815)
## 112) compactness_se< -3.468609 23 0 M (1.00000000 0.00000000) *
## 113) compactness_se>=-3.468609 4 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -3.979062 4 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean< -2.369786 38 8 B (0.21052632 0.78947368)
## 58) symmetry_worst>=-1.567876 8 3 M (0.62500000 0.37500000)
## 116) smoothness_worst< -1.513087 5 0 M (1.00000000 0.00000000) *
## 117) smoothness_worst>=-1.513087 3 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst< -1.567876 30 3 B (0.10000000 0.90000000)
## 118) smoothness_mean< -2.438762 9 3 B (0.33333333 0.66666667) *
## 119) smoothness_mean>=-2.438762 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.329341 85 4 B (0.04705882 0.95294118)
## 30) texture_mean>=3.019196 26 4 B (0.15384615 0.84615385)
## 60) smoothness_mean>=-2.257258 3 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.257258 23 1 B (0.04347826 0.95652174)
## 122) texture_mean< 3.028188 1 0 M (1.00000000 0.00000000) *
## 123) texture_mean>=3.028188 22 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.019196 59 0 B (0.00000000 1.00000000) *
##
## $trees[[57]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.4912281 0.5087719)
## 2) smoothness_mean>=-2.546123 876 432 M (0.5068493 0.4931507)
## 4) compactness_se>=-4.704842 862 418 M (0.5150812 0.4849188)
## 8) compactness_se< -4.098353 230 81 M (0.6478261 0.3521739)
## 16) smoothness_mean< -2.289681 203 58 M (0.7142857 0.2857143)
## 32) texture_worst>=4.362076 183 42 M (0.7704918 0.2295082)
## 64) symmetry_worst< -1.484766 169 31 M (0.8165680 0.1834320) *
## 65) symmetry_worst>=-1.484766 14 3 B (0.2142857 0.7857143) *
## 33) texture_worst< 4.362076 20 4 B (0.2000000 0.8000000)
## 66) compactness_se>=-4.193609 6 2 M (0.6666667 0.3333333) *
## 67) compactness_se< -4.193609 14 0 B (0.0000000 1.0000000) *
## 17) smoothness_mean>=-2.289681 27 4 B (0.1481481 0.8518519)
## 34) smoothness_mean>=-2.222419 7 3 M (0.5714286 0.4285714)
## 68) texture_mean>=2.892399 4 0 M (1.0000000 0.0000000) *
## 69) texture_mean< 2.892399 3 0 B (0.0000000 1.0000000) *
## 35) smoothness_mean< -2.222419 20 0 B (0.0000000 1.0000000) *
## 9) compactness_se>=-4.098353 632 295 B (0.4667722 0.5332278)
## 18) compactness_se>=-4.05446 599 295 B (0.4924875 0.5075125)
## 36) smoothness_worst>=-1.499656 287 110 M (0.6167247 0.3832753)
## 72) smoothness_worst< -1.434076 203 63 M (0.6896552 0.3103448) *
## 73) smoothness_worst>=-1.434076 84 37 B (0.4404762 0.5595238) *
## 37) smoothness_worst< -1.499656 312 118 B (0.3782051 0.6217949)
## 74) texture_worst>=4.569119 187 90 B (0.4812834 0.5187166) *
## 75) texture_worst< 4.569119 125 28 B (0.2240000 0.7760000) *
## 19) compactness_se< -4.05446 33 0 B (0.0000000 1.0000000) *
## 5) compactness_se< -4.704842 14 0 B (0.0000000 1.0000000) *
## 3) smoothness_mean< -2.546123 36 4 B (0.1111111 0.8888889)
## 6) smoothness_worst< -1.720903 6 2 M (0.6666667 0.3333333)
## 12) texture_mean< 3.103494 4 0 M (1.0000000 0.0000000) *
## 13) texture_mean>=3.103494 2 0 B (0.0000000 1.0000000) *
## 7) smoothness_worst>=-1.720903 30 0 B (0.0000000 1.0000000) *
##
## $trees[[58]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 443 B (0.48574561 0.51425439)
## 2) symmetry_worst>=-1.068249 22 0 M (1.00000000 0.00000000) *
## 3) symmetry_worst< -1.068249 890 421 B (0.47303371 0.52696629)
## 6) texture_mean>=2.927988 554 257 M (0.53610108 0.46389892)
## 12) smoothness_worst>=-1.473476 121 33 M (0.72727273 0.27272727)
## 24) texture_worst< 4.76475 42 2 M (0.95238095 0.04761905)
## 48) texture_mean>=2.934384 41 1 M (0.97560976 0.02439024)
## 96) smoothness_mean< -2.107265 40 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.107265 1 0 B (0.00000000 1.00000000) *
## 49) texture_mean< 2.934384 1 0 B (0.00000000 1.00000000) *
## 25) texture_worst>=4.76475 79 31 M (0.60759494 0.39240506)
## 50) texture_worst>=4.821213 69 21 M (0.69565217 0.30434783)
## 100) compactness_se< -3.379822 56 11 M (0.80357143 0.19642857) *
## 101) compactness_se>=-3.379822 13 3 B (0.23076923 0.76923077) *
## 51) texture_worst< 4.821213 10 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.473476 433 209 B (0.48267898 0.51732102)
## 26) smoothness_mean< -2.353249 289 125 M (0.56747405 0.43252595)
## 52) smoothness_mean>=-2.408446 98 23 M (0.76530612 0.23469388)
## 104) texture_worst< 4.876647 79 10 M (0.87341772 0.12658228) *
## 105) texture_worst>=4.876647 19 6 B (0.31578947 0.68421053) *
## 53) smoothness_mean< -2.408446 191 89 B (0.46596859 0.53403141)
## 106) texture_worst< 4.592462 53 9 M (0.83018868 0.16981132) *
## 107) texture_worst>=4.592462 138 45 B (0.32608696 0.67391304) *
## 27) smoothness_mean>=-2.353249 144 45 B (0.31250000 0.68750000)
## 54) smoothness_mean>=-2.303285 82 38 B (0.46341463 0.53658537)
## 108) compactness_se>=-3.470794 24 5 M (0.79166667 0.20833333) *
## 109) compactness_se< -3.470794 58 19 B (0.32758621 0.67241379) *
## 55) smoothness_mean< -2.303285 62 7 B (0.11290323 0.88709677)
## 110) texture_worst>=4.764475 14 7 M (0.50000000 0.50000000) *
## 111) texture_worst< 4.764475 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean< 2.927988 336 124 B (0.36904762 0.63095238)
## 14) symmetry_worst< -1.809006 150 70 M (0.53333333 0.46666667)
## 28) texture_worst< 4.400796 117 40 M (0.65811966 0.34188034)
## 56) smoothness_mean< -2.290163 98 21 M (0.78571429 0.21428571)
## 112) texture_mean< 2.903056 90 13 M (0.85555556 0.14444444) *
## 113) texture_mean>=2.903056 8 0 B (0.00000000 1.00000000) *
## 57) smoothness_mean>=-2.290163 19 0 B (0.00000000 1.00000000) *
## 29) texture_worst>=4.400796 33 3 B (0.09090909 0.90909091)
## 58) compactness_se< -4.431402 8 3 B (0.37500000 0.62500000)
## 116) compactness_se>=-4.50262 3 0 M (1.00000000 0.00000000) *
## 117) compactness_se< -4.50262 5 0 B (0.00000000 1.00000000) *
## 59) compactness_se>=-4.431402 25 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst>=-1.809006 186 44 B (0.23655914 0.76344086)
## 30) smoothness_worst>=-1.520499 118 39 B (0.33050847 0.66949153)
## 60) smoothness_worst< -1.517609 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_worst>=-1.517609 110 31 B (0.28181818 0.71818182)
## 122) symmetry_worst>=-1.641484 76 30 B (0.39473684 0.60526316) *
## 123) symmetry_worst< -1.641484 34 1 B (0.02941176 0.97058824) *
## 31) smoothness_worst< -1.520499 68 5 B (0.07352941 0.92647059)
## 62) compactness_se< -4.159844 19 5 B (0.26315789 0.73684211)
## 124) compactness_se>=-4.166611 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -4.166611 15 1 B (0.06666667 0.93333333) *
## 63) compactness_se>=-4.159844 49 0 B (0.00000000 1.00000000) *
##
## $trees[[59]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 M (0.52850877 0.47149123)
## 2) symmetry_worst>=-1.785734 501 194 M (0.61277445 0.38722555)
## 4) texture_mean>=2.806989 443 152 M (0.65688488 0.34311512)
## 8) compactness_se>=-3.955455 259 58 M (0.77606178 0.22393822)
## 16) compactness_se< -2.86687 242 47 M (0.80578512 0.19421488)
## 32) symmetry_worst< -1.128751 230 39 M (0.83043478 0.16956522)
## 64) texture_mean< 3.36829 227 36 M (0.84140969 0.15859031) *
## 65) texture_mean>=3.36829 3 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst>=-1.128751 12 4 B (0.33333333 0.66666667)
## 66) smoothness_worst>=-1.49848 4 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst< -1.49848 8 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-2.86687 17 6 B (0.35294118 0.64705882)
## 34) texture_mean>=3.050024 5 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.050024 12 1 B (0.08333333 0.91666667)
## 70) smoothness_mean>=-2.161865 1 0 M (1.00000000 0.00000000) *
## 71) smoothness_mean< -2.161865 11 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.955455 184 90 B (0.48913043 0.51086957)
## 18) symmetry_worst< -1.733593 18 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst>=-1.733593 166 72 B (0.43373494 0.56626506)
## 38) smoothness_worst< -1.607486 30 4 M (0.86666667 0.13333333)
## 76) texture_mean< 3.296262 26 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=3.296262 4 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.607486 136 46 B (0.33823529 0.66176471)
## 78) symmetry_worst>=-1.577444 81 39 B (0.48148148 0.51851852) *
## 79) symmetry_worst< -1.577444 55 7 B (0.12727273 0.87272727) *
## 5) texture_mean< 2.806989 58 16 B (0.27586207 0.72413793)
## 10) smoothness_mean>=-2.232593 15 3 M (0.80000000 0.20000000)
## 20) symmetry_worst< -1.492909 10 0 M (1.00000000 0.00000000) *
## 21) symmetry_worst>=-1.492909 5 2 B (0.40000000 0.60000000)
## 42) smoothness_mean< -2.229802 2 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean>=-2.229802 3 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.232593 43 4 B (0.09302326 0.90697674)
## 22) compactness_se>=-3.433945 8 4 M (0.50000000 0.50000000)
## 44) smoothness_worst>=-1.49622 4 0 M (1.00000000 0.00000000) *
## 45) smoothness_worst< -1.49622 4 0 B (0.00000000 1.00000000) *
## 23) compactness_se< -3.433945 35 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.785734 411 175 B (0.42579075 0.57420925)
## 6) smoothness_worst>=-1.603315 345 164 B (0.47536232 0.52463768)
## 12) smoothness_worst< -1.59596 28 1 M (0.96428571 0.03571429)
## 24) texture_mean>=2.85796 18 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.85796 10 1 M (0.90000000 0.10000000)
## 50) texture_mean< 2.793316 9 0 M (1.00000000 0.00000000) *
## 51) texture_mean>=2.793316 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst>=-1.59596 317 137 B (0.43217666 0.56782334)
## 26) texture_mean>=3.043808 126 47 M (0.62698413 0.37301587)
## 52) texture_mean< 3.21023 72 8 M (0.88888889 0.11111111)
## 104) smoothness_worst>=-1.583806 68 4 M (0.94117647 0.05882353) *
## 105) smoothness_worst< -1.583806 4 0 B (0.00000000 1.00000000) *
## 53) texture_mean>=3.21023 54 15 B (0.27777778 0.72222222)
## 106) texture_worst>=5.073596 29 14 M (0.51724138 0.48275862) *
## 107) texture_worst< 5.073596 25 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 3.043808 191 58 B (0.30366492 0.69633508)
## 54) smoothness_worst>=-1.500665 78 35 B (0.44871795 0.55128205)
## 108) smoothness_worst< -1.476411 35 10 M (0.71428571 0.28571429) *
## 109) smoothness_worst>=-1.476411 43 10 B (0.23255814 0.76744186) *
## 55) smoothness_worst< -1.500665 113 23 B (0.20353982 0.79646018)
## 110) smoothness_worst< -1.594361 5 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.594361 108 18 B (0.16666667 0.83333333) *
## 7) smoothness_worst< -1.603315 66 11 B (0.16666667 0.83333333)
## 14) compactness_se< -4.6643 15 6 M (0.60000000 0.40000000)
## 28) smoothness_mean< -2.535018 9 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.535018 6 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.6643 51 2 B (0.03921569 0.96078431)
## 30) smoothness_worst< -1.720903 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=3.026052 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 3.026052 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.720903 48 0 B (0.00000000 1.00000000) *
##
## $trees[[60]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 453 B (0.49671053 0.50328947)
## 2) symmetry_worst>=-1.424186 86 25 M (0.70930233 0.29069767)
## 4) texture_worst>=4.647183 42 4 M (0.90476190 0.09523810)
## 8) smoothness_worst>=-1.49649 36 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.49649 6 2 B (0.33333333 0.66666667)
## 18) smoothness_mean< -2.311841 2 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.311841 4 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.647183 44 21 M (0.52272727 0.47727273)
## 10) smoothness_mean>=-2.217831 15 2 M (0.86666667 0.13333333)
## 20) smoothness_mean< -2.022167 13 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean>=-2.022167 2 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.217831 29 10 B (0.34482759 0.65517241)
## 22) texture_worst< 4.136225 10 0 M (1.00000000 0.00000000) *
## 23) texture_worst>=4.136225 19 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.424186 826 392 B (0.47457627 0.52542373)
## 6) texture_worst>=4.260219 718 356 M (0.50417827 0.49582173)
## 12) compactness_se< -4.116284 223 83 M (0.62780269 0.37219731)
## 24) symmetry_worst< -1.508268 209 70 M (0.66507177 0.33492823)
## 48) compactness_se>=-4.705732 200 61 M (0.69500000 0.30500000)
## 96) texture_worst>=4.339889 194 55 M (0.71649485 0.28350515) *
## 97) texture_worst< 4.339889 6 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.705732 9 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst>=-1.508268 14 1 B (0.07142857 0.92857143)
## 50) compactness_se>=-4.234991 1 0 M (1.00000000 0.00000000) *
## 51) compactness_se< -4.234991 13 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-4.116284 495 222 B (0.44848485 0.55151515)
## 26) texture_mean< 2.747587 23 2 M (0.91304348 0.08695652)
## 52) texture_mean>=2.697516 21 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.697516 2 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.747587 472 201 B (0.42584746 0.57415254)
## 54) texture_mean>=2.927988 378 182 B (0.48148148 0.51851852)
## 108) texture_mean< 2.940483 29 1 M (0.96551724 0.03448276) *
## 109) texture_mean>=2.940483 349 154 B (0.44126074 0.55873926) *
## 55) texture_mean< 2.927988 94 19 B (0.20212766 0.79787234)
## 110) smoothness_worst>=-1.45348 13 4 M (0.69230769 0.30769231) *
## 111) smoothness_worst< -1.45348 81 10 B (0.12345679 0.87654321) *
## 7) texture_worst< 4.260219 108 30 B (0.27777778 0.72222222)
## 14) compactness_se>=-3.894783 70 30 B (0.42857143 0.57142857)
## 28) compactness_se< -3.48221 51 21 M (0.58823529 0.41176471)
## 56) texture_worst< 4.206328 37 7 M (0.81081081 0.18918919)
## 112) compactness_se>=-3.764682 22 0 M (1.00000000 0.00000000) *
## 113) compactness_se< -3.764682 15 7 M (0.53333333 0.46666667) *
## 57) texture_worst>=4.206328 14 0 B (0.00000000 1.00000000) *
## 29) compactness_se>=-3.48221 19 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.894783 38 0 B (0.00000000 1.00000000) *
##
## $trees[[61]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 392 M (0.57017544 0.42982456)
## 2) smoothness_worst>=-1.525694 520 182 M (0.65000000 0.35000000)
## 4) texture_mean>=3.06081 166 31 M (0.81325301 0.18674699)
## 8) smoothness_mean< -2.301586 103 10 M (0.90291262 0.09708738)
## 16) compactness_se< -3.106177 97 4 M (0.95876289 0.04123711)
## 32) compactness_se>=-4.507761 95 2 M (0.97894737 0.02105263)
## 64) texture_mean< 3.355261 86 0 M (1.00000000 0.00000000) *
## 65) texture_mean>=3.355261 9 2 M (0.77777778 0.22222222) *
## 33) compactness_se< -4.507761 2 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.106177 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean>=-2.301586 63 21 M (0.66666667 0.33333333)
## 18) smoothness_mean>=-2.257137 28 3 M (0.89285714 0.10714286)
## 36) smoothness_mean< -2.105484 25 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean>=-2.105484 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.257137 35 17 B (0.48571429 0.51428571)
## 38) texture_worst>=4.965981 9 0 M (1.00000000 0.00000000) *
## 39) texture_worst< 4.965981 26 8 B (0.30769231 0.69230769)
## 78) compactness_se>=-3.444069 6 0 M (1.00000000 0.00000000) *
## 79) compactness_se< -3.444069 20 2 B (0.10000000 0.90000000) *
## 5) texture_mean< 3.06081 354 151 M (0.57344633 0.42655367)
## 10) smoothness_worst>=-1.496637 287 106 M (0.63066202 0.36933798)
## 20) texture_mean< 2.987952 209 60 M (0.71291866 0.28708134)
## 40) symmetry_worst>=-1.864441 166 34 M (0.79518072 0.20481928)
## 80) texture_worst>=4.194566 145 21 M (0.85517241 0.14482759) *
## 81) texture_worst< 4.194566 21 8 B (0.38095238 0.61904762) *
## 41) symmetry_worst< -1.864441 43 17 B (0.39534884 0.60465116)
## 82) smoothness_worst< -1.480334 18 3 M (0.83333333 0.16666667) *
## 83) smoothness_worst>=-1.480334 25 2 B (0.08000000 0.92000000) *
## 21) texture_mean>=2.987952 78 32 B (0.41025641 0.58974359)
## 42) texture_worst>=4.874946 19 4 M (0.78947368 0.21052632)
## 84) compactness_se>=-4.030876 14 0 M (1.00000000 0.00000000) *
## 85) compactness_se< -4.030876 5 1 B (0.20000000 0.80000000) *
## 43) texture_worst< 4.874946 59 17 B (0.28813559 0.71186441)
## 86) compactness_se< -4.280193 7 1 M (0.85714286 0.14285714) *
## 87) compactness_se>=-4.280193 52 11 B (0.21153846 0.78846154) *
## 11) smoothness_worst< -1.496637 67 22 B (0.32835821 0.67164179)
## 22) symmetry_worst< -1.736492 39 18 M (0.53846154 0.46153846)
## 44) compactness_se< -3.210824 27 6 M (0.77777778 0.22222222)
## 88) compactness_se>=-3.979062 21 1 M (0.95238095 0.04761905) *
## 89) compactness_se< -3.979062 6 1 B (0.16666667 0.83333333) *
## 45) compactness_se>=-3.210824 12 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-1.736492 28 1 B (0.03571429 0.96428571)
## 46) texture_mean>=3.01402 1 0 M (1.00000000 0.00000000) *
## 47) texture_mean< 3.01402 27 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.525694 392 182 B (0.46428571 0.53571429)
## 6) compactness_se>=-3.610301 135 51 M (0.62222222 0.37777778)
## 12) smoothness_mean>=-2.396197 68 14 M (0.79411765 0.20588235)
## 24) smoothness_worst< -1.527573 64 10 M (0.84375000 0.15625000)
## 48) texture_worst>=4.411908 54 4 M (0.92592593 0.07407407)
## 96) smoothness_worst>=-1.618016 49 1 M (0.97959184 0.02040816) *
## 97) smoothness_worst< -1.618016 5 2 B (0.40000000 0.60000000) *
## 49) texture_worst< 4.411908 10 4 B (0.40000000 0.60000000)
## 98) compactness_se< -3.492332 4 0 M (1.00000000 0.00000000) *
## 99) compactness_se>=-3.492332 6 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst>=-1.527573 4 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.396197 67 30 B (0.44776119 0.55223881)
## 26) compactness_se< -3.593774 15 0 M (1.00000000 0.00000000) *
## 27) compactness_se>=-3.593774 52 15 B (0.28846154 0.71153846)
## 54) smoothness_worst>=-1.604936 29 14 B (0.48275862 0.51724138)
## 108) smoothness_worst< -1.594363 12 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst>=-1.594363 17 2 B (0.11764706 0.88235294) *
## 55) smoothness_worst< -1.604936 23 1 B (0.04347826 0.95652174)
## 110) smoothness_worst< -1.720903 1 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.720903 22 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.610301 257 98 B (0.38132296 0.61867704)
## 14) compactness_se< -4.579712 47 14 M (0.70212766 0.29787234)
## 28) compactness_se>=-4.711555 36 4 M (0.88888889 0.11111111)
## 56) smoothness_worst< -1.549205 33 1 M (0.96969697 0.03030303)
## 112) smoothness_worst>=-1.609211 25 0 M (1.00000000 0.00000000) *
## 113) smoothness_worst< -1.609211 8 1 M (0.87500000 0.12500000) *
## 57) smoothness_worst>=-1.549205 3 0 B (0.00000000 1.00000000) *
## 29) compactness_se< -4.711555 11 1 B (0.09090909 0.90909091)
## 58) symmetry_worst>=-1.179946 1 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.179946 10 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.579712 210 65 B (0.30952381 0.69047619)
## 30) smoothness_mean< -2.382983 153 61 B (0.39869281 0.60130719)
## 60) smoothness_mean>=-2.434747 69 25 M (0.63768116 0.36231884)
## 120) smoothness_worst< -1.538735 59 15 M (0.74576271 0.25423729) *
## 121) smoothness_worst>=-1.538735 10 0 B (0.00000000 1.00000000) *
## 61) smoothness_mean< -2.434747 84 17 B (0.20238095 0.79761905)
## 122) texture_worst< 4.447343 21 10 M (0.52380952 0.47619048) *
## 123) texture_worst>=4.447343 63 6 B (0.09523810 0.90476190) *
## 31) smoothness_mean>=-2.382983 57 4 B (0.07017544 0.92982456)
## 62) compactness_se>=-3.721403 10 4 B (0.40000000 0.60000000)
## 124) compactness_se< -3.62066 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.62066 6 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.721403 47 0 B (0.00000000 1.00000000) *
##
## $trees[[62]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 437 M (0.52083333 0.47916667)
## 2) texture_mean>=2.963467 519 198 M (0.61849711 0.38150289)
## 4) symmetry_worst>=-2.01934 408 134 M (0.67156863 0.32843137)
## 8) smoothness_worst>=-1.660611 399 125 M (0.68671679 0.31328321)
## 16) texture_worst>=5.06141 82 12 M (0.85365854 0.14634146)
## 32) symmetry_worst< -1.450078 73 6 M (0.91780822 0.08219178)
## 64) texture_worst< 5.386175 56 0 M (1.00000000 0.00000000) *
## 65) texture_worst>=5.386175 17 6 M (0.64705882 0.35294118) *
## 33) symmetry_worst>=-1.450078 9 3 B (0.33333333 0.66666667)
## 66) texture_mean< 3.217018 3 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.217018 6 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 5.06141 317 113 M (0.64353312 0.35646688)
## 34) smoothness_mean>=-2.42138 234 68 M (0.70940171 0.29059829)
## 68) smoothness_mean< -2.352368 64 7 M (0.89062500 0.10937500) *
## 69) smoothness_mean>=-2.352368 170 61 M (0.64117647 0.35882353) *
## 35) smoothness_mean< -2.42138 83 38 B (0.45783133 0.54216867)
## 70) compactness_se< -4.014684 46 13 M (0.71739130 0.28260870) *
## 71) compactness_se>=-4.014684 37 5 B (0.13513514 0.86486486) *
## 9) smoothness_worst< -1.660611 9 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -2.01934 111 47 B (0.42342342 0.57657658)
## 10) compactness_se>=-3.551587 48 15 M (0.68750000 0.31250000)
## 20) smoothness_worst>=-1.601489 36 6 M (0.83333333 0.16666667)
## 40) texture_worst< 5.216315 32 2 M (0.93750000 0.06250000)
## 80) texture_mean>=3.049609 30 0 M (1.00000000 0.00000000) *
## 81) texture_mean< 3.049609 2 0 B (0.00000000 1.00000000) *
## 41) texture_worst>=5.216315 4 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.601489 12 3 B (0.25000000 0.75000000)
## 42) texture_worst< 4.59024 4 1 M (0.75000000 0.25000000)
## 84) texture_mean>=3.032942 3 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 3.032942 1 0 B (0.00000000 1.00000000) *
## 43) texture_worst>=4.59024 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -3.551587 63 14 B (0.22222222 0.77777778)
## 22) texture_mean< 3.038878 8 1 M (0.87500000 0.12500000)
## 44) texture_mean>=3.000441 7 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 3.000441 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=3.038878 55 7 B (0.12727273 0.87272727)
## 46) smoothness_mean>=-2.290398 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.290398 54 6 B (0.11111111 0.88888889)
## 94) symmetry_worst>=-2.052205 17 5 B (0.29411765 0.70588235) *
## 95) symmetry_worst< -2.052205 37 1 B (0.02702703 0.97297297) *
## 3) texture_mean< 2.963467 393 154 B (0.39185751 0.60814249)
## 6) smoothness_worst>=-1.451541 71 15 M (0.78873239 0.21126761)
## 12) smoothness_mean< -2.240129 38 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean>=-2.240129 33 15 M (0.54545455 0.45454545)
## 26) texture_mean< 2.757784 17 3 M (0.82352941 0.17647059)
## 52) smoothness_mean< -1.889548 16 2 M (0.87500000 0.12500000)
## 104) compactness_se>=-3.896708 15 1 M (0.93333333 0.06666667) *
## 105) compactness_se< -3.896708 1 0 B (0.00000000 1.00000000) *
## 53) smoothness_mean>=-1.889548 1 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.757784 16 4 B (0.25000000 0.75000000)
## 54) texture_mean>=2.935178 3 0 M (1.00000000 0.00000000) *
## 55) texture_mean< 2.935178 13 1 B (0.07692308 0.92307692)
## 110) smoothness_worst< -1.437625 1 0 M (1.00000000 0.00000000) *
## 111) smoothness_worst>=-1.437625 12 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.451541 322 98 B (0.30434783 0.69565217)
## 14) symmetry_worst>=-1.327359 23 6 M (0.73913043 0.26086957)
## 28) symmetry_worst< -1.23578 13 0 M (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.23578 10 4 B (0.40000000 0.60000000)
## 58) smoothness_worst< -1.461111 4 0 M (1.00000000 0.00000000) *
## 59) smoothness_worst>=-1.461111 6 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.327359 299 81 B (0.27090301 0.72909699)
## 30) smoothness_mean< -2.441446 77 38 M (0.50649351 0.49350649)
## 60) symmetry_worst>=-1.816978 43 9 M (0.79069767 0.20930233)
## 120) texture_worst< 4.644924 38 4 M (0.89473684 0.10526316) *
## 121) texture_worst>=4.644924 5 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst< -1.816978 34 5 B (0.14705882 0.85294118)
## 122) smoothness_mean>=-2.443746 5 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.443746 29 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.441446 222 42 B (0.18918919 0.81081081)
## 62) compactness_se< -4.116284 58 20 B (0.34482759 0.65517241)
## 124) compactness_se>=-4.201715 17 2 M (0.88235294 0.11764706) *
## 125) compactness_se< -4.201715 41 5 B (0.12195122 0.87804878) *
## 63) compactness_se>=-4.116284 164 22 B (0.13414634 0.86585366)
## 126) smoothness_worst>=-1.472307 12 5 B (0.41666667 0.58333333) *
## 127) smoothness_worst< -1.472307 152 17 B (0.11184211 0.88815789) *
##
## $trees[[63]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 M (0.51535088 0.48464912)
## 2) texture_mean>=3.065024 297 112 M (0.62289562 0.37710438)
## 4) smoothness_worst>=-1.618721 268 86 M (0.67910448 0.32089552)
## 8) texture_worst< 4.781945 55 1 M (0.98181818 0.01818182)
## 16) texture_worst>=4.52814 54 0 M (1.00000000 0.00000000) *
## 17) texture_worst< 4.52814 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst>=4.781945 213 85 M (0.60093897 0.39906103)
## 18) texture_worst>=4.820212 185 63 M (0.65945946 0.34054054)
## 36) symmetry_worst>=-1.71268 74 13 M (0.82432432 0.17567568)
## 72) smoothness_mean>=-2.509617 71 10 M (0.85915493 0.14084507) *
## 73) smoothness_mean< -2.509617 3 0 B (0.00000000 1.00000000) *
## 37) symmetry_worst< -1.71268 111 50 M (0.54954955 0.45045045)
## 74) symmetry_worst< -1.733593 91 30 M (0.67032967 0.32967033) *
## 75) symmetry_worst>=-1.733593 20 0 B (0.00000000 1.00000000) *
## 19) texture_worst< 4.820212 28 6 B (0.21428571 0.78571429)
## 38) compactness_se>=-3.052779 3 0 M (1.00000000 0.00000000) *
## 39) compactness_se< -3.052779 25 3 B (0.12000000 0.88000000)
## 78) smoothness_worst>=-1.444063 3 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.444063 22 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.618721 29 3 B (0.10344828 0.89655172)
## 10) texture_worst< 4.609308 4 1 M (0.75000000 0.25000000)
## 20) texture_mean>=3.075433 3 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.075433 1 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.609308 25 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 3.065024 615 285 B (0.46341463 0.53658537)
## 6) smoothness_worst>=-1.472307 174 62 M (0.64367816 0.35632184)
## 12) compactness_se>=-4.032549 123 32 M (0.73983740 0.26016260)
## 24) compactness_se< -3.931945 30 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-3.931945 93 32 M (0.65591398 0.34408602)
## 50) symmetry_worst< -1.486964 68 16 M (0.76470588 0.23529412)
## 100) symmetry_worst>=-1.828219 53 5 M (0.90566038 0.09433962) *
## 101) symmetry_worst< -1.828219 15 4 B (0.26666667 0.73333333) *
## 51) symmetry_worst>=-1.486964 25 9 B (0.36000000 0.64000000)
## 102) smoothness_mean>=-2.287745 16 7 M (0.56250000 0.43750000) *
## 103) smoothness_mean< -2.287745 9 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -4.032549 51 21 B (0.41176471 0.58823529)
## 26) smoothness_worst< -1.458214 22 2 M (0.90909091 0.09090909)
## 52) texture_mean>=2.901883 20 0 M (1.00000000 0.00000000) *
## 53) texture_mean< 2.901883 2 0 B (0.00000000 1.00000000) *
## 27) smoothness_worst>=-1.458214 29 1 B (0.03448276 0.96551724)
## 54) symmetry_worst< -1.741496 4 1 B (0.25000000 0.75000000)
## 108) symmetry_worst>=-1.780237 1 0 M (1.00000000 0.00000000) *
## 109) symmetry_worst< -1.780237 3 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst>=-1.741496 25 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.472307 441 173 B (0.39229025 0.60770975)
## 14) smoothness_worst< -1.476997 400 171 B (0.42750000 0.57250000)
## 28) smoothness_worst>=-1.482701 34 3 M (0.91176471 0.08823529)
## 56) compactness_se>=-4.290267 32 1 M (0.96875000 0.03125000)
## 112) texture_mean>=2.732378 31 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.732378 1 0 B (0.00000000 1.00000000) *
## 57) compactness_se< -4.290267 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst< -1.482701 366 140 B (0.38251366 0.61748634)
## 58) compactness_se< -3.476676 299 133 B (0.44481605 0.55518395)
## 116) compactness_se>=-3.716111 71 9 M (0.87323944 0.12676056) *
## 117) compactness_se< -3.716111 228 71 B (0.31140351 0.68859649) *
## 59) compactness_se>=-3.476676 67 7 B (0.10447761 0.89552239)
## 118) symmetry_worst>=-1.474719 7 1 M (0.85714286 0.14285714) *
## 119) symmetry_worst< -1.474719 60 1 B (0.01666667 0.98333333) *
## 15) smoothness_worst>=-1.476997 41 2 B (0.04878049 0.95121951)
## 30) texture_worst>=4.844547 2 0 M (1.00000000 0.00000000) *
## 31) texture_worst< 4.844547 39 0 B (0.00000000 1.00000000) *
##
## $trees[[64]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) compactness_se< -4.49319 106 31 M (0.70754717 0.29245283)
## 4) compactness_se>=-4.704842 95 20 M (0.78947368 0.21052632)
## 8) symmetry_worst< -1.509002 87 12 M (0.86206897 0.13793103)
## 16) texture_mean< 3.232565 84 9 M (0.89285714 0.10714286)
## 32) texture_mean>=2.846651 82 7 M (0.91463415 0.08536585)
## 64) smoothness_mean< -2.295268 80 5 M (0.93750000 0.06250000) *
## 65) smoothness_mean>=-2.295268 2 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.846651 2 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=3.232565 3 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.509002 8 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -4.704842 11 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-4.49319 806 358 B (0.44416873 0.55583127)
## 6) smoothness_worst>=-1.559144 598 295 B (0.49331104 0.50668896)
## 12) symmetry_worst< -1.781339 272 109 M (0.59926471 0.40073529)
## 24) smoothness_mean< -2.313857 158 42 M (0.73417722 0.26582278)
## 48) compactness_se< -3.455891 144 29 M (0.79861111 0.20138889)
## 96) symmetry_worst>=-2.233349 134 19 M (0.85820896 0.14179104) *
## 97) symmetry_worst< -2.233349 10 0 B (0.00000000 1.00000000) *
## 49) compactness_se>=-3.455891 14 1 B (0.07142857 0.92857143)
## 98) smoothness_mean< -2.465359 1 0 M (1.00000000 0.00000000) *
## 99) smoothness_mean>=-2.465359 13 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.313857 114 47 B (0.41228070 0.58771930)
## 50) smoothness_worst>=-1.499656 76 33 M (0.56578947 0.43421053)
## 100) smoothness_mean< -2.219625 49 13 M (0.73469388 0.26530612) *
## 101) smoothness_mean>=-2.219625 27 7 B (0.25925926 0.74074074) *
## 51) smoothness_worst< -1.499656 38 4 B (0.10526316 0.89473684)
## 102) compactness_se>=-3.239565 6 3 M (0.50000000 0.50000000) *
## 103) compactness_se< -3.239565 32 1 B (0.03125000 0.96875000) *
## 13) symmetry_worst>=-1.781339 326 132 B (0.40490798 0.59509202)
## 26) symmetry_worst>=-1.524537 86 36 M (0.58139535 0.41860465)
## 52) texture_mean>=2.777879 68 20 M (0.70588235 0.29411765)
## 104) symmetry_worst< -1.124686 55 10 M (0.81818182 0.18181818) *
## 105) symmetry_worst>=-1.124686 13 3 B (0.23076923 0.76923077) *
## 53) texture_mean< 2.777879 18 2 B (0.11111111 0.88888889)
## 106) compactness_se>=-3.173162 2 0 M (1.00000000 0.00000000) *
## 107) compactness_se< -3.173162 16 0 B (0.00000000 1.00000000) *
## 27) symmetry_worst< -1.524537 240 82 B (0.34166667 0.65833333)
## 54) smoothness_mean>=-2.413908 195 78 B (0.40000000 0.60000000)
## 108) smoothness_worst< -1.531349 15 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst>=-1.531349 180 63 B (0.35000000 0.65000000) *
## 55) smoothness_mean< -2.413908 45 4 B (0.08888889 0.91111111)
## 110) texture_worst>=5.003123 4 0 M (1.00000000 0.00000000) *
## 111) texture_worst< 5.003123 41 0 B (0.00000000 1.00000000) *
## 7) smoothness_worst< -1.559144 208 63 B (0.30288462 0.69711538)
## 14) smoothness_mean>=-2.302636 14 2 M (0.85714286 0.14285714)
## 28) compactness_se>=-3.929833 12 0 M (1.00000000 0.00000000) *
## 29) compactness_se< -3.929833 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.302636 194 51 B (0.26288660 0.73711340)
## 30) symmetry_worst>=-1.538661 25 8 M (0.68000000 0.32000000)
## 60) texture_mean>=2.989073 17 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.989073 8 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.538661 169 34 B (0.20118343 0.79881657)
## 62) compactness_se>=-3.489046 38 16 B (0.42105263 0.57894737)
## 124) texture_mean>=3.136493 8 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.136493 30 8 B (0.26666667 0.73333333) *
## 63) compactness_se< -3.489046 131 18 B (0.13740458 0.86259542)
## 126) texture_worst< 4.679785 66 18 B (0.27272727 0.72727273) *
## 127) texture_worst>=4.679785 65 0 B (0.00000000 1.00000000) *
##
## $trees[[65]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 435 M (0.52302632 0.47697368)
## 2) texture_worst>=4.260219 805 357 M (0.55652174 0.44347826)
## 4) smoothness_worst>=-1.424105 61 9 M (0.85245902 0.14754098)
## 8) smoothness_mean>=-2.361754 57 5 M (0.91228070 0.08771930)
## 16) compactness_se>=-4.130421 56 4 M (0.92857143 0.07142857)
## 32) smoothness_mean< -2.093138 48 1 M (0.97916667 0.02083333)
## 64) smoothness_mean< -2.170242 39 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.170242 9 1 M (0.88888889 0.11111111) *
## 33) smoothness_mean>=-2.093138 8 3 M (0.62500000 0.37500000)
## 66) texture_mean< 2.970462 5 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=2.970462 3 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.130421 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.361754 4 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.424105 744 348 M (0.53225806 0.46774194)
## 10) smoothness_mean< -2.261445 642 281 M (0.56230530 0.43769470)
## 20) symmetry_worst>=-2.01934 506 196 M (0.61264822 0.38735178)
## 40) smoothness_worst>=-1.52112 258 74 M (0.71317829 0.28682171)
## 80) texture_worst< 4.545891 84 10 M (0.88095238 0.11904762) *
## 81) texture_worst>=4.545891 174 64 M (0.63218391 0.36781609) *
## 41) smoothness_worst< -1.52112 248 122 M (0.50806452 0.49193548)
## 82) symmetry_worst>=-1.549706 56 16 M (0.71428571 0.28571429) *
## 83) symmetry_worst< -1.549706 192 86 B (0.44791667 0.55208333) *
## 21) symmetry_worst< -2.01934 136 51 B (0.37500000 0.62500000)
## 42) symmetry_worst< -2.49184 17 1 M (0.94117647 0.05882353)
## 84) texture_mean< 3.310501 16 0 M (1.00000000 0.00000000) *
## 85) texture_mean>=3.310501 1 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst>=-2.49184 119 35 B (0.29411765 0.70588235)
## 86) smoothness_mean< -2.352958 83 33 B (0.39759036 0.60240964) *
## 87) smoothness_mean>=-2.352958 36 2 B (0.05555556 0.94444444) *
## 11) smoothness_mean>=-2.261445 102 35 B (0.34313725 0.65686275)
## 22) smoothness_mean>=-2.201842 16 2 M (0.87500000 0.12500000)
## 44) texture_worst>=4.450297 14 0 M (1.00000000 0.00000000) *
## 45) texture_worst< 4.450297 2 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.201842 86 21 B (0.24418605 0.75581395)
## 46) texture_mean>=3.050442 13 4 M (0.69230769 0.30769231)
## 92) compactness_se>=-4.008292 9 0 M (1.00000000 0.00000000) *
## 93) compactness_se< -4.008292 4 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 3.050442 73 12 B (0.16438356 0.83561644)
## 94) symmetry_worst>=-1.802807 41 12 B (0.29268293 0.70731707) *
## 95) symmetry_worst< -1.802807 32 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.260219 107 29 B (0.27102804 0.72897196)
## 6) symmetry_worst>=-1.567877 22 7 M (0.68181818 0.31818182)
## 12) texture_mean>=2.756192 10 0 M (1.00000000 0.00000000) *
## 13) texture_mean< 2.756192 12 5 B (0.41666667 0.58333333)
## 26) texture_mean< 2.518783 4 0 M (1.00000000 0.00000000) *
## 27) texture_mean>=2.518783 8 1 B (0.12500000 0.87500000)
## 54) compactness_se>=-3.3026 1 0 M (1.00000000 0.00000000) *
## 55) compactness_se< -3.3026 7 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.567877 85 14 B (0.16470588 0.83529412)
## 14) smoothness_worst< -1.54469 39 12 B (0.30769231 0.69230769)
## 28) smoothness_worst>=-1.545117 6 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.545117 33 6 B (0.18181818 0.81818182)
## 58) texture_mean>=2.764104 12 6 M (0.50000000 0.50000000)
## 116) compactness_se>=-3.607729 6 0 M (1.00000000 0.00000000) *
## 117) compactness_se< -3.607729 6 0 B (0.00000000 1.00000000) *
## 59) texture_mean< 2.764104 21 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.54469 46 2 B (0.04347826 0.95652174)
## 30) compactness_se< -3.823116 17 2 B (0.11764706 0.88235294)
## 60) compactness_se>=-3.894783 2 0 M (1.00000000 0.00000000) *
## 61) compactness_se< -3.894783 15 0 B (0.00000000 1.00000000) *
## 31) compactness_se>=-3.823116 29 0 B (0.00000000 1.00000000) *
##
## $trees[[66]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 425 M (0.53399123 0.46600877)
## 2) texture_mean>=2.824054 799 353 M (0.55819775 0.44180225)
## 4) texture_worst< 4.644679 402 144 M (0.64179104 0.35820896)
## 8) smoothness_worst>=-1.451542 57 2 M (0.96491228 0.03508772)
## 16) texture_worst>=4.226553 56 1 M (0.98214286 0.01785714)
## 32) smoothness_worst< -1.349735 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst>=-1.349735 2 1 M (0.50000000 0.50000000)
## 66) texture_mean>=2.957438 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 2.957438 1 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.226553 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.451542 345 142 M (0.58840580 0.41159420)
## 18) smoothness_worst< -1.476997 312 111 M (0.64423077 0.35576923)
## 36) texture_worst>=4.614897 56 4 M (0.92857143 0.07142857)
## 72) compactness_se>=-4.694501 53 1 M (0.98113208 0.01886792) *
## 73) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 37) texture_worst< 4.614897 256 107 M (0.58203125 0.41796875)
## 74) symmetry_worst< -1.561818 223 80 M (0.64125561 0.35874439) *
## 75) symmetry_worst>=-1.561818 33 6 B (0.18181818 0.81818182) *
## 19) smoothness_worst>=-1.476997 33 2 B (0.06060606 0.93939394)
## 38) texture_mean>=2.932513 3 1 M (0.66666667 0.33333333)
## 76) texture_mean< 2.963141 2 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=2.963141 1 0 B (0.00000000 1.00000000) *
## 39) texture_mean< 2.932513 30 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.644679 397 188 B (0.47355164 0.52644836)
## 10) symmetry_worst>=-1.660659 184 71 M (0.61413043 0.38586957)
## 20) symmetry_worst< -1.608146 43 2 M (0.95348837 0.04651163)
## 40) texture_mean>=2.970637 42 1 M (0.97619048 0.02380952)
## 80) texture_mean< 3.129266 41 0 M (1.00000000 0.00000000) *
## 81) texture_mean>=3.129266 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.970637 1 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst>=-1.608146 141 69 M (0.51063830 0.48936170)
## 42) smoothness_mean>=-2.281815 28 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.281815 113 44 B (0.38938053 0.61061947)
## 86) compactness_se< -4.081893 36 10 M (0.72222222 0.27777778) *
## 87) compactness_se>=-4.081893 77 18 B (0.23376623 0.76623377) *
## 11) symmetry_worst< -1.660659 213 75 B (0.35211268 0.64788732)
## 22) texture_worst>=4.837624 125 59 B (0.47200000 0.52800000)
## 44) texture_worst< 4.985267 25 2 M (0.92000000 0.08000000)
## 88) symmetry_worst>=-2.207844 24 1 M (0.95833333 0.04166667) *
## 89) symmetry_worst< -2.207844 1 0 B (0.00000000 1.00000000) *
## 45) texture_worst>=4.985267 100 36 B (0.36000000 0.64000000)
## 90) compactness_se>=-4.248059 63 30 B (0.47619048 0.52380952) *
## 91) compactness_se< -4.248059 37 6 B (0.16216216 0.83783784) *
## 23) texture_worst< 4.837624 88 16 B (0.18181818 0.81818182)
## 46) compactness_se>=-2.790746 6 0 M (1.00000000 0.00000000) *
## 47) compactness_se< -2.790746 82 10 B (0.12195122 0.87804878)
## 94) symmetry_worst< -2.121358 22 9 B (0.40909091 0.59090909) *
## 95) symmetry_worst>=-2.121358 60 1 B (0.01666667 0.98333333) *
## 3) texture_mean< 2.824054 113 41 B (0.36283186 0.63716814)
## 6) compactness_se>=-3.964431 83 41 B (0.49397590 0.50602410)
## 12) texture_worst< 4.328009 73 32 M (0.56164384 0.43835616)
## 24) texture_worst>=3.804403 63 23 M (0.63492063 0.36507937)
## 48) texture_mean< 2.771335 45 11 M (0.75555556 0.24444444)
## 96) compactness_se>=-3.891799 43 9 M (0.79069767 0.20930233) *
## 97) compactness_se< -3.891799 2 0 B (0.00000000 1.00000000) *
## 49) texture_mean>=2.771335 18 6 B (0.33333333 0.66666667)
## 98) texture_mean>=2.811204 6 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.811204 12 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 3.804403 10 1 B (0.10000000 0.90000000)
## 50) smoothness_mean< -2.298096 2 1 M (0.50000000 0.50000000)
## 100) texture_mean< 2.673405 1 0 M (1.00000000 0.00000000) *
## 101) texture_mean>=2.673405 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_mean>=-2.298096 8 0 B (0.00000000 1.00000000) *
## 13) texture_worst>=4.328009 10 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.964431 30 0 B (0.00000000 1.00000000) *
##
## $trees[[67]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 M (0.50877193 0.49122807)
## 2) smoothness_mean>=-2.546123 895 431 M (0.51843575 0.48156425)
## 4) texture_worst>=4.572846 507 213 M (0.57988166 0.42011834)
## 8) smoothness_worst< -1.484675 342 116 M (0.66081871 0.33918129)
## 16) smoothness_mean>=-2.403622 165 34 M (0.79393939 0.20606061)
## 32) symmetry_worst>=-2.207988 153 22 M (0.85620915 0.14379085)
## 64) smoothness_mean< -2.286221 131 9 M (0.93129771 0.06870229) *
## 65) smoothness_mean>=-2.286221 22 9 B (0.40909091 0.59090909) *
## 33) symmetry_worst< -2.207988 12 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean< -2.403622 177 82 M (0.53672316 0.46327684)
## 34) smoothness_worst< -1.568573 83 21 M (0.74698795 0.25301205)
## 68) symmetry_worst>=-1.966444 71 11 M (0.84507042 0.15492958) *
## 69) symmetry_worst< -1.966444 12 2 B (0.16666667 0.83333333) *
## 35) smoothness_worst>=-1.568573 94 33 B (0.35106383 0.64893617)
## 70) texture_worst< 4.982438 56 27 M (0.51785714 0.48214286) *
## 71) texture_worst>=4.982438 38 4 B (0.10526316 0.89473684) *
## 9) smoothness_worst>=-1.484675 165 68 B (0.41212121 0.58787879)
## 18) smoothness_mean>=-2.284747 59 21 M (0.64406780 0.35593220)
## 36) compactness_se>=-4.032549 44 9 M (0.79545455 0.20454545)
## 72) smoothness_mean< -2.093138 37 2 M (0.94594595 0.05405405) *
## 73) smoothness_mean>=-2.093138 7 0 B (0.00000000 1.00000000) *
## 37) compactness_se< -4.032549 15 3 B (0.20000000 0.80000000)
## 74) texture_mean< 2.979048 5 2 M (0.60000000 0.40000000) *
## 75) texture_mean>=2.979048 10 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.284747 106 30 B (0.28301887 0.71698113)
## 38) texture_worst< 4.624204 5 0 M (1.00000000 0.00000000) *
## 39) texture_worst>=4.624204 101 25 B (0.24752475 0.75247525)
## 78) symmetry_worst>=-1.650994 55 23 B (0.41818182 0.58181818) *
## 79) symmetry_worst< -1.650994 46 2 B (0.04347826 0.95652174) *
## 5) texture_worst< 4.572846 388 170 B (0.43814433 0.56185567)
## 10) texture_worst< 4.54138 343 167 B (0.48688047 0.51311953)
## 20) smoothness_worst>=-1.451731 63 16 M (0.74603175 0.25396825)
## 40) compactness_se>=-4.086695 55 8 M (0.85454545 0.14545455)
## 80) symmetry_worst< -1.395041 46 3 M (0.93478261 0.06521739) *
## 81) symmetry_worst>=-1.395041 9 4 B (0.44444444 0.55555556) *
## 41) compactness_se< -4.086695 8 0 B (0.00000000 1.00000000) *
## 21) smoothness_worst< -1.451731 280 120 B (0.42857143 0.57142857)
## 42) symmetry_worst< -2.401622 17 0 M (1.00000000 0.00000000) *
## 43) symmetry_worst>=-2.401622 263 103 B (0.39163498 0.60836502)
## 86) texture_worst>=4.535341 14 0 M (1.00000000 0.00000000) *
## 87) texture_worst< 4.535341 249 89 B (0.35742972 0.64257028) *
## 11) texture_worst>=4.54138 45 3 B (0.06666667 0.93333333)
## 22) smoothness_mean>=-2.282906 6 3 M (0.50000000 0.50000000)
## 44) texture_mean>=2.943507 3 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.943507 3 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean< -2.282906 39 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.546123 17 0 B (0.00000000 1.00000000) *
##
## $trees[[68]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 400 M (0.56140351 0.43859649)
## 2) compactness_se>=-3.721197 376 131 M (0.65159574 0.34840426)
## 4) symmetry_worst>=-1.892495 256 69 M (0.73046875 0.26953125)
## 8) texture_worst>=3.969009 249 63 M (0.74698795 0.25301205)
## 16) compactness_se< -3.494301 97 13 M (0.86597938 0.13402062)
## 32) smoothness_mean>=-2.380711 63 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean< -2.380711 34 13 M (0.61764706 0.38235294)
## 66) compactness_se< -3.5866 25 4 M (0.84000000 0.16000000) *
## 67) compactness_se>=-3.5866 9 0 B (0.00000000 1.00000000) *
## 17) compactness_se>=-3.494301 152 50 M (0.67105263 0.32894737)
## 34) compactness_se>=-3.484318 131 29 M (0.77862595 0.22137405)
## 68) compactness_se< -2.919705 112 19 M (0.83035714 0.16964286) *
## 69) compactness_se>=-2.919705 19 9 B (0.47368421 0.52631579) *
## 35) compactness_se< -3.484318 21 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 3.969009 7 1 B (0.14285714 0.85714286)
## 18) texture_mean< 2.366153 1 0 M (1.00000000 0.00000000) *
## 19) texture_mean>=2.366153 6 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.892495 120 58 B (0.48333333 0.51666667)
## 10) symmetry_worst< -1.982941 78 25 M (0.67948718 0.32051282)
## 20) symmetry_worst>=-2.174839 40 4 M (0.90000000 0.10000000)
## 40) texture_mean< 3.304787 38 2 M (0.94736842 0.05263158)
## 80) smoothness_worst>=-1.604936 32 0 M (1.00000000 0.00000000) *
## 81) smoothness_worst< -1.604936 6 2 M (0.66666667 0.33333333) *
## 41) texture_mean>=3.304787 2 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -2.174839 38 17 B (0.44736842 0.55263158)
## 42) smoothness_mean< -2.437515 13 1 M (0.92307692 0.07692308)
## 84) smoothness_mean>=-2.490273 12 0 M (1.00000000 0.00000000) *
## 85) smoothness_mean< -2.490273 1 0 B (0.00000000 1.00000000) *
## 43) smoothness_mean>=-2.437515 25 5 B (0.20000000 0.80000000)
## 86) texture_mean>=3.190563 9 4 M (0.55555556 0.44444444) *
## 87) texture_mean< 3.190563 16 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst>=-1.982941 42 5 B (0.11904762 0.88095238)
## 22) texture_mean>=3.088324 5 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.088324 37 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.721197 536 267 B (0.49813433 0.50186567)
## 6) compactness_se< -3.859436 458 209 M (0.54366812 0.45633188)
## 12) texture_mean>=2.934384 269 100 M (0.62825279 0.37174721)
## 24) compactness_se>=-4.28781 168 45 M (0.73214286 0.26785714)
## 48) smoothness_mean< -2.290664 130 23 M (0.82307692 0.17692308)
## 96) compactness_se< -3.869459 125 18 M (0.85600000 0.14400000) *
## 97) compactness_se>=-3.869459 5 0 B (0.00000000 1.00000000) *
## 49) smoothness_mean>=-2.290664 38 16 B (0.42105263 0.57894737)
## 98) smoothness_mean>=-2.251921 15 2 M (0.86666667 0.13333333) *
## 99) smoothness_mean< -2.251921 23 3 B (0.13043478 0.86956522) *
## 25) compactness_se< -4.28781 101 46 B (0.45544554 0.54455446)
## 50) texture_mean< 3.227241 71 25 M (0.64788732 0.35211268)
## 100) compactness_se< -4.335534 64 18 M (0.71875000 0.28125000) *
## 101) compactness_se>=-4.335534 7 0 B (0.00000000 1.00000000) *
## 51) texture_mean>=3.227241 30 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 2.934384 189 80 B (0.42328042 0.57671958)
## 26) texture_mean< 2.898946 155 77 B (0.49677419 0.50322581)
## 52) texture_mean>=2.876103 48 10 M (0.79166667 0.20833333)
## 104) symmetry_worst< -1.701169 41 3 M (0.92682927 0.07317073) *
## 105) symmetry_worst>=-1.701169 7 0 B (0.00000000 1.00000000) *
## 53) texture_mean< 2.876103 107 39 B (0.36448598 0.63551402)
## 106) smoothness_worst>=-1.542984 68 29 M (0.57352941 0.42647059) *
## 107) smoothness_worst< -1.542984 39 0 B (0.00000000 1.00000000) *
## 27) texture_mean>=2.898946 34 3 B (0.08823529 0.91176471)
## 54) texture_worst>=4.707428 2 0 M (1.00000000 0.00000000) *
## 55) texture_worst< 4.707428 32 1 B (0.03125000 0.96875000)
## 110) compactness_se< -4.680858 1 0 M (1.00000000 0.00000000) *
## 111) compactness_se>=-4.680858 31 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.859436 78 18 B (0.23076923 0.76923077)
## 14) smoothness_worst>=-1.480731 33 15 M (0.54545455 0.45454545)
## 28) texture_mean>=2.971675 12 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.971675 21 6 B (0.28571429 0.71428571)
## 58) symmetry_worst>=-1.612049 6 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.612049 15 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.480731 45 0 B (0.00000000 1.00000000) *
##
## $trees[[69]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 455 B (0.49890351 0.50109649)
## 2) smoothness_mean>=-2.416986 622 279 M (0.55144695 0.44855305)
## 4) smoothness_mean< -2.349943 198 63 M (0.68181818 0.31818182)
## 8) symmetry_worst>=-2.212871 182 47 M (0.74175824 0.25824176)
## 16) texture_worst>=4.613791 108 11 M (0.89814815 0.10185185)
## 32) smoothness_mean>=-2.408892 104 7 M (0.93269231 0.06730769)
## 64) texture_mean< 3.36829 96 2 M (0.97916667 0.02083333) *
## 65) texture_mean>=3.36829 8 3 B (0.37500000 0.62500000) *
## 33) smoothness_mean< -2.408892 4 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.613791 74 36 M (0.51351351 0.48648649)
## 34) texture_mean>=2.97527 18 1 M (0.94444444 0.05555556)
## 68) texture_mean< 3.041522 17 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.041522 1 0 B (0.00000000 1.00000000) *
## 35) texture_mean< 2.97527 56 21 B (0.37500000 0.62500000)
## 70) smoothness_worst>=-1.545556 29 8 M (0.72413793 0.27586207) *
## 71) smoothness_worst< -1.545556 27 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.212871 16 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.349943 424 208 B (0.49056604 0.50943396)
## 10) smoothness_mean>=-2.332582 378 171 M (0.54761905 0.45238095)
## 20) compactness_se>=-3.027402 41 3 M (0.92682927 0.07317073)
## 40) compactness_se< -2.455682 38 1 M (0.97368421 0.02631579)
## 80) smoothness_worst>=-1.554815 31 0 M (1.00000000 0.00000000) *
## 81) smoothness_worst< -1.554815 7 1 M (0.85714286 0.14285714) *
## 41) compactness_se>=-2.455682 3 1 B (0.33333333 0.66666667)
## 82) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 2.915767 2 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.027402 337 168 M (0.50148368 0.49851632)
## 42) smoothness_worst< -1.562856 27 1 M (0.96296296 0.03703704)
## 84) smoothness_mean< -2.277089 23 0 M (1.00000000 0.00000000) *
## 85) smoothness_mean>=-2.277089 4 1 M (0.75000000 0.25000000) *
## 43) smoothness_worst>=-1.562856 310 143 B (0.46129032 0.53870968)
## 86) texture_worst>=5.073596 13 0 M (1.00000000 0.00000000) *
## 87) texture_worst< 5.073596 297 130 B (0.43771044 0.56228956) *
## 11) smoothness_mean< -2.332582 46 1 B (0.02173913 0.97826087)
## 22) symmetry_worst< -2.154356 1 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst>=-2.154356 45 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.416986 290 112 B (0.38620690 0.61379310)
## 6) compactness_se>=-4.350232 200 94 B (0.47000000 0.53000000)
## 12) compactness_se< -4.283814 28 1 M (0.96428571 0.03571429)
## 24) smoothness_worst>=-1.654625 27 0 M (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.654625 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-4.283814 172 67 B (0.38953488 0.61046512)
## 26) texture_worst>=5.003123 29 6 M (0.79310345 0.20689655)
## 52) smoothness_mean>=-2.492372 26 3 M (0.88461538 0.11538462)
## 104) texture_worst< 5.316369 20 0 M (1.00000000 0.00000000) *
## 105) texture_worst>=5.316369 6 3 M (0.50000000 0.50000000) *
## 53) smoothness_mean< -2.492372 3 0 B (0.00000000 1.00000000) *
## 27) texture_worst< 5.003123 143 44 B (0.30769231 0.69230769)
## 54) smoothness_worst< -1.598711 59 28 M (0.52542373 0.47457627)
## 108) symmetry_worst>=-2.050132 40 11 M (0.72500000 0.27500000) *
## 109) symmetry_worst< -2.050132 19 2 B (0.10526316 0.89473684) *
## 55) smoothness_worst>=-1.598711 84 13 B (0.15476190 0.84523810)
## 110) symmetry_worst< -1.995409 6 1 M (0.83333333 0.16666667) *
## 111) symmetry_worst>=-1.995409 78 8 B (0.10256410 0.89743590) *
## 7) compactness_se< -4.350232 90 18 B (0.20000000 0.80000000)
## 14) texture_mean>=3.124472 21 10 B (0.47619048 0.52380952)
## 28) texture_mean< 3.17309 10 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.17309 11 0 B (0.00000000 1.00000000) *
## 15) texture_mean< 3.124472 69 8 B (0.11594203 0.88405797)
## 30) symmetry_worst>=-1.658507 26 7 B (0.26923077 0.73076923)
## 60) smoothness_mean< -2.503847 4 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.503847 22 3 B (0.13636364 0.86363636)
## 122) texture_mean< 2.936149 4 1 M (0.75000000 0.25000000) *
## 123) texture_mean>=2.936149 18 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst< -1.658507 43 1 B (0.02325581 0.97674419)
## 62) compactness_se< -4.6643 6 1 B (0.16666667 0.83333333)
## 124) compactness_se>=-4.740419 1 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -4.740419 5 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-4.6643 37 0 B (0.00000000 1.00000000) *
##
## $trees[[70]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 444 M (0.51315789 0.48684211)
## 2) smoothness_mean>=-2.332634 422 163 M (0.61374408 0.38625592)
## 4) smoothness_mean< -2.31481 70 10 M (0.85714286 0.14285714)
## 8) texture_mean>=2.849464 56 2 M (0.96428571 0.03571429)
## 16) compactness_se< -3.515615 42 0 M (1.00000000 0.00000000) *
## 17) compactness_se>=-3.515615 14 2 M (0.85714286 0.14285714)
## 34) compactness_se>=-3.342347 12 0 M (1.00000000 0.00000000) *
## 35) compactness_se< -3.342347 2 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 2.849464 14 6 B (0.42857143 0.57142857)
## 18) smoothness_mean>=-2.322851 6 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean< -2.322851 8 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.31481 352 153 M (0.56534091 0.43465909)
## 10) compactness_se>=-4.222363 312 119 M (0.61858974 0.38141026)
## 20) smoothness_mean>=-2.303285 292 99 M (0.66095890 0.33904110)
## 40) symmetry_worst>=-1.775265 181 45 M (0.75138122 0.24861878)
## 80) texture_mean>=2.777879 159 32 M (0.79874214 0.20125786) *
## 81) texture_mean< 2.777879 22 9 B (0.40909091 0.59090909) *
## 41) symmetry_worst< -1.775265 111 54 M (0.51351351 0.48648649)
## 82) smoothness_worst< -1.433708 94 38 M (0.59574468 0.40425532) *
## 83) smoothness_worst>=-1.433708 17 1 B (0.05882353 0.94117647) *
## 21) smoothness_mean< -2.303285 20 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.222363 40 6 B (0.15000000 0.85000000)
## 22) smoothness_mean< -2.3007 6 0 M (1.00000000 0.00000000) *
## 23) smoothness_mean>=-2.3007 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.332634 490 209 B (0.42653061 0.57346939)
## 6) smoothness_mean< -2.349943 451 204 B (0.45232816 0.54767184)
## 12) smoothness_mean>=-2.357834 19 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.357834 432 185 B (0.42824074 0.57175926)
## 26) texture_mean>=2.763153 411 185 B (0.45012165 0.54987835)
## 52) texture_worst< 4.3976 64 21 M (0.67187500 0.32812500)
## 104) smoothness_worst>=-1.554805 33 2 M (0.93939394 0.06060606) *
## 105) smoothness_worst< -1.554805 31 12 B (0.38709677 0.61290323) *
## 53) texture_worst>=4.3976 347 142 B (0.40922190 0.59077810)
## 106) smoothness_mean>=-2.396732 78 30 M (0.61538462 0.38461538) *
## 107) smoothness_mean< -2.396732 269 94 B (0.34944238 0.65055762) *
## 27) texture_mean< 2.763153 21 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.349943 39 5 B (0.12820513 0.87179487)
## 14) smoothness_worst>=-1.435092 2 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst< -1.435092 37 3 B (0.08108108 0.91891892)
## 30) symmetry_worst< -2.189951 3 1 M (0.66666667 0.33333333)
## 60) texture_mean< 3.025767 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.025767 1 0 B (0.00000000 1.00000000) *
## 31) symmetry_worst>=-2.189951 34 1 B (0.02941176 0.97058824)
## 62) symmetry_worst>=-1.41845 7 1 B (0.14285714 0.85714286)
## 124) texture_mean>=2.986903 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.986903 6 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.41845 27 0 B (0.00000000 1.00000000) *
##
## $trees[[71]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.51096491 0.48903509)
## 2) symmetry_worst>=-1.840831 571 247 M (0.56742557 0.43257443)
## 4) compactness_se>=-3.690481 250 83 M (0.66800000 0.33200000)
## 8) smoothness_worst< -1.434262 188 48 M (0.74468085 0.25531915)
## 16) symmetry_worst< -1.128751 167 32 M (0.80838323 0.19161677)
## 32) smoothness_mean>=-2.503795 163 28 M (0.82822086 0.17177914)
## 64) texture_worst>=4.56463 93 8 M (0.91397849 0.08602151) *
## 65) texture_worst< 4.56463 70 20 M (0.71428571 0.28571429) *
## 33) smoothness_mean< -2.503795 4 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.128751 21 5 B (0.23809524 0.76190476)
## 34) symmetry_worst>=-1.068249 5 0 M (1.00000000 0.00000000) *
## 35) symmetry_worst< -1.068249 16 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst>=-1.434262 62 27 B (0.43548387 0.56451613)
## 18) compactness_se< -3.470851 17 2 M (0.88235294 0.11764706)
## 36) texture_mean>=2.688296 15 0 M (1.00000000 0.00000000) *
## 37) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-3.470851 45 12 B (0.26666667 0.73333333)
## 38) symmetry_worst>=-1.306254 5 0 M (1.00000000 0.00000000) *
## 39) symmetry_worst< -1.306254 40 7 B (0.17500000 0.82500000)
## 78) texture_worst< 4.846274 21 7 B (0.33333333 0.66666667) *
## 79) texture_worst>=4.846274 19 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.690481 321 157 B (0.48909657 0.51090343)
## 10) smoothness_worst< -1.576769 53 13 M (0.75471698 0.24528302)
## 20) smoothness_mean< -2.496118 34 2 M (0.94117647 0.05882353)
## 40) texture_mean< 3.17207 32 0 M (1.00000000 0.00000000) *
## 41) texture_mean>=3.17207 2 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean>=-2.496118 19 8 B (0.42105263 0.57894737)
## 42) smoothness_mean>=-2.434747 6 0 M (1.00000000 0.00000000) *
## 43) smoothness_mean< -2.434747 13 2 B (0.15384615 0.84615385)
## 86) texture_mean>=3.31519 1 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.31519 12 1 B (0.08333333 0.91666667) *
## 11) smoothness_worst>=-1.576769 268 117 B (0.43656716 0.56343284)
## 22) smoothness_worst>=-1.556321 236 117 B (0.49576271 0.50423729)
## 44) compactness_se< -3.859436 200 89 M (0.55500000 0.44500000)
## 88) smoothness_mean>=-2.473387 190 79 M (0.58421053 0.41578947) *
## 89) smoothness_mean< -2.473387 10 0 B (0.00000000 1.00000000) *
## 45) compactness_se>=-3.859436 36 6 B (0.16666667 0.83333333)
## 90) smoothness_worst>=-1.455217 8 2 M (0.75000000 0.25000000) *
## 91) smoothness_worst< -1.455217 28 0 B (0.00000000 1.00000000) *
## 23) smoothness_worst< -1.556321 32 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.840831 341 142 B (0.41642229 0.58357771)
## 6) symmetry_worst< -1.925345 261 129 B (0.49425287 0.50574713)
## 12) symmetry_worst>=-2.052205 122 46 M (0.62295082 0.37704918)
## 24) smoothness_mean>=-2.449526 101 28 M (0.72277228 0.27722772)
## 48) smoothness_worst< -1.540225 51 1 M (0.98039216 0.01960784)
## 96) compactness_se< -3.451641 50 0 M (1.00000000 0.00000000) *
## 97) compactness_se>=-3.451641 1 0 B (0.00000000 1.00000000) *
## 49) smoothness_worst>=-1.540225 50 23 B (0.46000000 0.54000000)
## 98) symmetry_worst< -1.990435 19 4 M (0.78947368 0.21052632) *
## 99) symmetry_worst>=-1.990435 31 8 B (0.25806452 0.74193548) *
## 25) smoothness_mean< -2.449526 21 3 B (0.14285714 0.85714286)
## 50) smoothness_worst>=-1.503558 3 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst< -1.503558 18 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -2.052205 139 53 B (0.38129496 0.61870504)
## 26) compactness_se>=-3.487878 36 10 M (0.72222222 0.27777778)
## 52) texture_mean>=3.049609 28 2 M (0.92857143 0.07142857)
## 104) smoothness_mean>=-2.661875 27 1 M (0.96296296 0.03703704) *
## 105) smoothness_mean< -2.661875 1 0 B (0.00000000 1.00000000) *
## 53) texture_mean< 3.049609 8 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.487878 103 27 B (0.26213592 0.73786408)
## 54) smoothness_mean>=-2.30775 25 6 M (0.76000000 0.24000000)
## 108) smoothness_worst>=-1.497846 14 0 M (1.00000000 0.00000000) *
## 109) smoothness_worst< -1.497846 11 5 B (0.45454545 0.54545455) *
## 55) smoothness_mean< -2.30775 78 8 B (0.10256410 0.89743590)
## 110) smoothness_worst>=-1.549837 28 8 B (0.28571429 0.71428571) *
## 111) smoothness_worst< -1.549837 50 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-1.925345 80 13 B (0.16250000 0.83750000)
## 14) texture_worst>=4.937311 13 3 M (0.76923077 0.23076923)
## 28) compactness_se>=-4.899363 10 0 M (1.00000000 0.00000000) *
## 29) compactness_se< -4.899363 3 0 B (0.00000000 1.00000000) *
## 15) texture_worst< 4.937311 67 3 B (0.04477612 0.95522388)
## 30) smoothness_worst>=-1.424105 2 1 M (0.50000000 0.50000000)
## 60) texture_mean< 2.876957 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=2.876957 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.424105 65 2 B (0.03076923 0.96923077)
## 62) smoothness_mean< -2.386198 14 2 B (0.14285714 0.85714286)
## 124) smoothness_mean>=-2.406561 2 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean< -2.406561 12 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.386198 51 0 B (0.00000000 1.00000000) *
##
## $trees[[72]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) texture_mean>=2.892314 675 335 B (0.49629630 0.50370370)
## 4) symmetry_worst< -2.379234 18 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst>=-2.379234 657 317 B (0.48249619 0.51750381)
## 10) smoothness_mean< -2.473552 106 36 M (0.66037736 0.33962264)
## 20) texture_mean>=2.935975 97 27 M (0.72164948 0.27835052)
## 40) texture_mean< 3.15715 72 11 M (0.84722222 0.15277778)
## 80) compactness_se< -2.82386 69 8 M (0.88405797 0.11594203) *
## 81) compactness_se>=-2.82386 3 0 B (0.00000000 1.00000000) *
## 41) texture_mean>=3.15715 25 9 B (0.36000000 0.64000000)
## 82) smoothness_mean>=-2.489159 10 1 M (0.90000000 0.10000000) *
## 83) smoothness_mean< -2.489159 15 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.935975 9 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.473552 551 247 B (0.44827586 0.55172414)
## 22) smoothness_worst>=-1.609811 516 246 B (0.47674419 0.52325581)
## 44) symmetry_worst>=-2.193154 471 234 M (0.50318471 0.49681529)
## 88) symmetry_worst< -2.115313 18 0 M (1.00000000 0.00000000) *
## 89) symmetry_worst>=-2.115313 453 219 B (0.48344371 0.51655629) *
## 45) symmetry_worst< -2.193154 45 9 B (0.20000000 0.80000000)
## 90) smoothness_mean< -2.447413 5 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean>=-2.447413 40 4 B (0.10000000 0.90000000) *
## 23) smoothness_worst< -1.609811 35 1 B (0.02857143 0.97142857)
## 46) smoothness_mean>=-2.337942 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.337942 34 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.892314 237 75 B (0.31645570 0.68354430)
## 6) compactness_se>=-3.891799 118 53 B (0.44915254 0.55084746)
## 12) smoothness_worst>=-1.482701 62 24 M (0.61290323 0.38709677)
## 24) symmetry_worst>=-1.732707 37 7 M (0.81081081 0.18918919)
## 48) symmetry_worst< -1.395292 20 0 M (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.395292 17 7 M (0.58823529 0.41176471)
## 98) symmetry_worst>=-1.281003 11 1 M (0.90909091 0.09090909) *
## 99) symmetry_worst< -1.281003 6 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -1.732707 25 8 B (0.32000000 0.68000000)
## 50) smoothness_worst< -1.478176 8 0 M (1.00000000 0.00000000) *
## 51) smoothness_worst>=-1.478176 17 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.482701 56 15 B (0.26785714 0.73214286)
## 26) texture_worst< 3.919786 15 5 M (0.66666667 0.33333333)
## 52) smoothness_mean>=-2.461945 11 1 M (0.90909091 0.09090909)
## 104) smoothness_mean< -2.298096 10 0 M (1.00000000 0.00000000) *
## 105) smoothness_mean>=-2.298096 1 0 B (0.00000000 1.00000000) *
## 53) smoothness_mean< -2.461945 4 0 B (0.00000000 1.00000000) *
## 27) texture_worst>=3.919786 41 5 B (0.12195122 0.87804878)
## 54) texture_mean< 2.717337 6 1 M (0.83333333 0.16666667)
## 108) symmetry_worst>=-1.940832 5 0 M (1.00000000 0.00000000) *
## 109) symmetry_worst< -1.940832 1 0 B (0.00000000 1.00000000) *
## 55) texture_mean>=2.717337 35 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.891799 119 22 B (0.18487395 0.81512605)
## 14) compactness_se< -4.159844 54 18 B (0.33333333 0.66666667)
## 28) compactness_se>=-4.198706 14 2 M (0.85714286 0.14285714)
## 56) texture_mean>=2.772165 12 0 M (1.00000000 0.00000000) *
## 57) texture_mean< 2.772165 2 0 B (0.00000000 1.00000000) *
## 29) compactness_se< -4.198706 40 6 B (0.15000000 0.85000000)
## 58) smoothness_worst< -1.546636 7 1 M (0.85714286 0.14285714)
## 116) texture_mean>=2.85389 6 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.85389 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_worst>=-1.546636 33 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.159844 65 4 B (0.06153846 0.93846154)
## 30) smoothness_worst>=-1.451541 8 4 M (0.50000000 0.50000000)
## 60) texture_mean>=2.803301 4 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.803301 4 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.451541 57 0 B (0.00000000 1.00000000) *
##
## $trees[[73]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 411 M (0.54934211 0.45065789)
## 2) texture_mean>=2.892591 690 269 M (0.61014493 0.38985507)
## 4) texture_worst< 4.753106 382 117 M (0.69371728 0.30628272)
## 8) texture_mean>=3.055881 73 7 M (0.90410959 0.09589041)
## 16) compactness_se>=-4.572499 70 4 M (0.94285714 0.05714286)
## 32) smoothness_worst>=-1.606352 54 0 M (1.00000000 0.00000000) *
## 33) smoothness_worst< -1.606352 16 4 M (0.75000000 0.25000000)
## 66) smoothness_worst< -1.693722 12 0 M (1.00000000 0.00000000) *
## 67) smoothness_worst>=-1.693722 4 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.572499 3 0 B (0.00000000 1.00000000) *
## 9) texture_mean< 3.055881 309 110 M (0.64401294 0.35598706)
## 18) smoothness_worst>=-1.473478 53 5 M (0.90566038 0.09433962)
## 36) texture_mean>=2.934384 49 2 M (0.95918367 0.04081633)
## 72) texture_mean< 3.039982 46 0 M (1.00000000 0.00000000) *
## 73) texture_mean>=3.039982 3 1 B (0.33333333 0.66666667) *
## 37) texture_mean< 2.934384 4 1 B (0.25000000 0.75000000)
## 74) smoothness_mean< -2.240603 1 0 M (1.00000000 0.00000000) *
## 75) smoothness_mean>=-2.240603 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.473478 256 105 M (0.58984375 0.41015625)
## 38) smoothness_worst< -1.476997 239 88 M (0.63179916 0.36820084)
## 76) smoothness_mean< -2.234468 204 62 M (0.69607843 0.30392157) *
## 77) smoothness_mean>=-2.234468 35 9 B (0.25714286 0.74285714) *
## 39) smoothness_worst>=-1.476997 17 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.753106 308 152 M (0.50649351 0.49350649)
## 10) texture_worst>=4.818867 254 107 M (0.57874016 0.42125984)
## 20) smoothness_mean>=-2.462871 198 66 M (0.66666667 0.33333333)
## 40) symmetry_worst>=-2.207988 184 53 M (0.71195652 0.28804348)
## 80) smoothness_worst< -1.447185 130 21 M (0.83846154 0.16153846) *
## 81) smoothness_worst>=-1.447185 54 22 B (0.40740741 0.59259259) *
## 41) symmetry_worst< -2.207988 14 1 B (0.07142857 0.92857143)
## 82) smoothness_mean>=-2.282229 1 0 M (1.00000000 0.00000000) *
## 83) smoothness_mean< -2.282229 13 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.462871 56 15 B (0.26785714 0.73214286)
## 42) symmetry_worst< -1.635915 27 12 M (0.55555556 0.44444444)
## 84) texture_mean< 3.17309 14 2 M (0.85714286 0.14285714) *
## 85) texture_mean>=3.17309 13 3 B (0.23076923 0.76923077) *
## 43) symmetry_worst>=-1.635915 29 0 B (0.00000000 1.00000000) *
## 11) texture_worst< 4.818867 54 9 B (0.16666667 0.83333333)
## 22) symmetry_worst>=-0.9904278 4 0 M (1.00000000 0.00000000) *
## 23) symmetry_worst< -0.9904278 50 5 B (0.10000000 0.90000000)
## 46) compactness_se>=-3.322755 4 1 M (0.75000000 0.25000000)
## 92) smoothness_mean>=-2.522464 3 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean< -2.522464 1 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.322755 46 2 B (0.04347826 0.95652174)
## 94) texture_worst< 4.781945 9 2 B (0.22222222 0.77777778) *
## 95) texture_worst>=4.781945 37 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.892591 222 80 B (0.36036036 0.63963964)
## 6) smoothness_mean>=-2.31958 117 58 M (0.50427350 0.49572650)
## 12) texture_mean< 2.844609 71 23 M (0.67605634 0.32394366)
## 24) texture_worst>=4.1745 40 6 M (0.85000000 0.15000000)
## 48) symmetry_worst>=-1.987693 38 4 M (0.89473684 0.10526316)
## 96) smoothness_mean< -2.238735 22 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.238735 16 4 M (0.75000000 0.25000000) *
## 49) symmetry_worst< -1.987693 2 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.1745 31 14 B (0.45161290 0.54838710)
## 50) smoothness_mean< -2.298594 7 0 M (1.00000000 0.00000000) *
## 51) smoothness_mean>=-2.298594 24 7 B (0.29166667 0.70833333)
## 102) symmetry_worst>=-1.612049 10 3 M (0.70000000 0.30000000) *
## 103) symmetry_worst< -1.612049 14 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=2.844609 46 11 B (0.23913043 0.76086957)
## 26) texture_worst>=4.669441 4 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.669441 42 7 B (0.16666667 0.83333333)
## 54) texture_worst< 4.361241 11 4 M (0.63636364 0.36363636)
## 108) texture_mean>=2.857891 8 1 M (0.87500000 0.12500000) *
## 109) texture_mean< 2.857891 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.361241 31 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.31958 105 21 B (0.20000000 0.80000000)
## 14) compactness_se< -4.31315 21 10 M (0.52380952 0.47619048)
## 28) texture_mean>=2.871852 8 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.871852 13 3 B (0.23076923 0.76923077)
## 58) smoothness_mean>=-2.372437 5 2 M (0.60000000 0.40000000)
## 116) texture_mean>=2.800736 3 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.800736 2 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.372437 8 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.31315 84 10 B (0.11904762 0.88095238)
## 30) smoothness_worst>=-1.452493 4 1 M (0.75000000 0.25000000)
## 60) texture_mean>=2.597803 3 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.597803 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.452493 80 7 B (0.08750000 0.91250000)
## 62) compactness_se>=-3.488718 22 7 B (0.31818182 0.68181818)
## 124) compactness_se< -3.483667 4 0 M (1.00000000 0.00000000) *
## 125) compactness_se>=-3.483667 18 3 B (0.16666667 0.83333333) *
## 63) compactness_se< -3.488718 58 0 B (0.00000000 1.00000000) *
##
## $trees[[74]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 447 B (0.49013158 0.50986842)
## 2) texture_mean>=2.963467 511 206 M (0.59686888 0.40313112)
## 4) smoothness_worst>=-1.579228 402 137 M (0.65920398 0.34079602)
## 8) smoothness_mean>=-2.501158 393 128 M (0.67430025 0.32569975)
## 16) smoothness_worst< -1.568787 27 0 M (1.00000000 0.00000000) *
## 17) smoothness_worst>=-1.568787 366 128 M (0.65027322 0.34972678)
## 34) smoothness_worst>=-1.561324 355 118 M (0.66760563 0.33239437)
## 68) smoothness_worst< -1.532606 55 6 M (0.89090909 0.10909091) *
## 69) smoothness_worst>=-1.532606 300 112 M (0.62666667 0.37333333) *
## 35) smoothness_worst< -1.561324 11 1 B (0.09090909 0.90909091)
## 70) compactness_se>=-2.682598 1 0 M (1.00000000 0.00000000) *
## 71) compactness_se< -2.682598 10 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.501158 9 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.579228 109 40 B (0.36697248 0.63302752)
## 10) symmetry_worst>=-1.693879 35 9 M (0.74285714 0.25714286)
## 20) compactness_se< -3.885202 21 0 M (1.00000000 0.00000000) *
## 21) compactness_se>=-3.885202 14 5 B (0.35714286 0.64285714)
## 42) compactness_se>=-3.153142 5 0 M (1.00000000 0.00000000) *
## 43) compactness_se< -3.153142 9 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.693879 74 14 B (0.18918919 0.81081081)
## 22) symmetry_worst< -2.081905 19 9 M (0.52631579 0.47368421)
## 44) compactness_se>=-3.424051 10 2 M (0.80000000 0.20000000)
## 88) smoothness_mean>=-2.638103 8 0 M (1.00000000 0.00000000) *
## 89) smoothness_mean< -2.638103 2 0 B (0.00000000 1.00000000) *
## 45) compactness_se< -3.424051 9 2 B (0.22222222 0.77777778)
## 90) smoothness_mean>=-2.373466 2 0 M (1.00000000 0.00000000) *
## 91) smoothness_mean< -2.373466 7 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst>=-2.081905 55 4 B (0.07272727 0.92727273)
## 46) texture_mean< 2.969886 2 0 M (1.00000000 0.00000000) *
## 47) texture_mean>=2.969886 53 2 B (0.03773585 0.96226415)
## 94) texture_mean>=3.172196 5 2 B (0.40000000 0.60000000) *
## 95) texture_mean< 3.172196 48 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.963467 401 142 B (0.35411471 0.64588529)
## 6) symmetry_worst>=-1.325507 22 3 M (0.86363636 0.13636364)
## 12) smoothness_mean>=-2.340715 20 1 M (0.95000000 0.05000000)
## 24) compactness_se< -2.588521 19 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-2.588521 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.340715 2 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.325507 379 123 B (0.32453826 0.67546174)
## 14) compactness_se< -3.426516 323 120 B (0.37151703 0.62848297)
## 28) compactness_se>=-3.764682 83 34 M (0.59036145 0.40963855)
## 56) symmetry_worst>=-1.813857 50 11 M (0.78000000 0.22000000)
## 112) texture_worst>=4.256309 30 0 M (1.00000000 0.00000000) *
## 113) texture_worst< 4.256309 20 9 B (0.45000000 0.55000000) *
## 57) symmetry_worst< -1.813857 33 10 B (0.30303030 0.69696970)
## 114) texture_worst< 4.000974 9 2 M (0.77777778 0.22222222) *
## 115) texture_worst>=4.000974 24 3 B (0.12500000 0.87500000) *
## 29) compactness_se< -3.764682 240 71 B (0.29583333 0.70416667)
## 58) smoothness_mean>=-2.391331 150 57 B (0.38000000 0.62000000)
## 116) texture_worst>=4.389172 100 48 M (0.52000000 0.48000000) *
## 117) texture_worst< 4.389172 50 5 B (0.10000000 0.90000000) *
## 59) smoothness_mean< -2.391331 90 14 B (0.15555556 0.84444444)
## 118) texture_worst< 4.411124 16 4 M (0.75000000 0.25000000) *
## 119) texture_worst>=4.411124 74 2 B (0.02702703 0.97297297) *
## 15) compactness_se>=-3.426516 56 3 B (0.05357143 0.94642857)
## 30) smoothness_mean>=-2.154617 4 2 M (0.50000000 0.50000000)
## 60) texture_mean>=2.720927 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.720927 2 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean< -2.154617 52 1 B (0.01923077 0.98076923)
## 62) symmetry_worst< -1.783471 6 1 B (0.16666667 0.83333333)
## 124) texture_mean>=2.902347 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 2.902347 5 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-1.783471 46 0 B (0.00000000 1.00000000) *
##
## $trees[[75]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 390 B (0.4276316 0.5723684)
## 2) texture_worst>=5.402766 22 0 M (1.0000000 0.0000000) *
## 3) texture_worst< 5.402766 890 368 B (0.4134831 0.5865169)
## 6) smoothness_mean>=-2.173316 62 18 M (0.7096774 0.2903226)
## 12) smoothness_worst< -1.409741 29 0 M (1.0000000 0.0000000) *
## 13) smoothness_worst>=-1.409741 33 15 B (0.4545455 0.5454545)
## 26) symmetry_worst>=-1.656121 22 7 M (0.6818182 0.3181818)
## 52) texture_mean>=2.688296 15 0 M (1.0000000 0.0000000) *
## 53) texture_mean< 2.688296 7 0 B (0.0000000 1.0000000) *
## 27) symmetry_worst< -1.656121 11 0 B (0.0000000 1.0000000) *
## 7) smoothness_mean< -2.173316 828 324 B (0.3913043 0.6086957)
## 14) symmetry_worst>=-1.001713 11 0 M (1.0000000 0.0000000) *
## 15) symmetry_worst< -1.001713 817 313 B (0.3831089 0.6168911)
## 30) compactness_se>=-4.505325 733 299 B (0.4079127 0.5920873)
## 60) compactness_se< -4.49319 14 0 M (1.0000000 0.0000000) *
## 61) compactness_se>=-4.49319 719 285 B (0.3963839 0.6036161)
## 122) texture_worst>=4.905415 138 59 M (0.5724638 0.4275362) *
## 123) texture_worst< 4.905415 581 206 B (0.3545611 0.6454389) *
## 31) compactness_se< -4.505325 84 14 B (0.1666667 0.8333333)
## 62) symmetry_worst< -2.374205 6 0 M (1.0000000 0.0000000) *
## 63) symmetry_worst>=-2.374205 78 8 B (0.1025641 0.8974359)
## 126) smoothness_mean< -2.449246 28 8 B (0.2857143 0.7142857) *
## 127) smoothness_mean>=-2.449246 50 0 B (0.0000000 1.0000000) *
##
## $trees[[76]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 438 B (0.48026316 0.51973684)
## 2) compactness_se>=-4.694501 893 438 B (0.49048152 0.50951848)
## 4) texture_worst>=4.275472 782 381 M (0.51278772 0.48721228)
## 8) smoothness_mean>=-2.201842 46 10 M (0.78260870 0.21739130)
## 16) smoothness_mean< -2.093138 40 5 M (0.87500000 0.12500000)
## 32) compactness_se>=-4.24572 38 3 M (0.92105263 0.07894737)
## 64) texture_mean>=2.918531 31 0 M (1.00000000 0.00000000) *
## 65) texture_mean< 2.918531 7 3 M (0.57142857 0.42857143) *
## 33) compactness_se< -4.24572 2 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.093138 6 1 B (0.16666667 0.83333333)
## 34) texture_mean< 2.894137 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean>=2.894137 5 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.201842 736 365 B (0.49592391 0.50407609)
## 18) smoothness_mean< -2.235394 690 332 M (0.51884058 0.48115942)
## 36) texture_worst< 4.550789 191 60 M (0.68586387 0.31413613)
## 72) compactness_se< -2.751692 180 49 M (0.72777778 0.27222222) *
## 73) compactness_se>=-2.751692 11 0 B (0.00000000 1.00000000) *
## 37) texture_worst>=4.550789 499 227 B (0.45490982 0.54509018)
## 74) texture_worst>=4.572846 457 223 B (0.48796499 0.51203501) *
## 75) texture_worst< 4.572846 42 4 B (0.09523810 0.90476190) *
## 19) smoothness_mean>=-2.235394 46 7 B (0.15217391 0.84782609)
## 38) texture_mean>=3.04949 4 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 3.04949 42 3 B (0.07142857 0.92857143)
## 78) texture_worst< 4.329277 1 0 M (1.00000000 0.00000000) *
## 79) texture_worst>=4.329277 41 2 B (0.04878049 0.95121951) *
## 5) texture_worst< 4.275472 111 37 B (0.33333333 0.66666667)
## 10) texture_worst< 4.18243 70 34 B (0.48571429 0.51428571)
## 20) compactness_se>=-3.97985 59 25 M (0.57627119 0.42372881)
## 40) compactness_se< -3.48221 41 12 M (0.70731707 0.29268293)
## 80) smoothness_mean>=-2.466148 37 8 M (0.78378378 0.21621622) *
## 81) smoothness_mean< -2.466148 4 0 B (0.00000000 1.00000000) *
## 41) compactness_se>=-3.48221 18 5 B (0.27777778 0.72222222)
## 82) smoothness_worst>=-1.490036 5 0 M (1.00000000 0.00000000) *
## 83) smoothness_worst< -1.490036 13 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.97985 11 0 B (0.00000000 1.00000000) *
## 11) texture_worst>=4.18243 41 3 B (0.07317073 0.92682927)
## 22) texture_mean< 2.715026 3 1 M (0.66666667 0.33333333)
## 44) texture_mean>=2.710705 2 0 M (1.00000000 0.00000000) *
## 45) texture_mean< 2.710705 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=2.715026 38 1 B (0.02631579 0.97368421)
## 46) smoothness_mean>=-2.15202 1 0 M (1.00000000 0.00000000) *
## 47) smoothness_mean< -2.15202 37 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -4.694501 19 0 B (0.00000000 1.00000000) *
##
## $trees[[77]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 M (0.51754386 0.48245614)
## 2) compactness_se>=-4.198706 699 310 M (0.55650930 0.44349070)
## 4) texture_worst>=4.481821 528 209 M (0.60416667 0.39583333)
## 8) symmetry_worst>=-1.43353 43 4 M (0.90697674 0.09302326)
## 16) texture_mean< 3.110611 37 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.110611 6 2 B (0.33333333 0.66666667)
## 34) texture_mean>=3.146047 2 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.146047 4 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -1.43353 485 205 M (0.57731959 0.42268041)
## 18) compactness_se< -4.116284 38 3 M (0.92105263 0.07894737)
## 36) smoothness_mean>=-2.434347 35 0 M (1.00000000 0.00000000) *
## 37) smoothness_mean< -2.434347 3 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-4.116284 447 202 M (0.54809843 0.45190157)
## 38) texture_mean>=2.892591 426 183 M (0.57042254 0.42957746)
## 76) compactness_se< -4.094455 19 0 M (1.00000000 0.00000000) *
## 77) compactness_se>=-4.094455 407 183 M (0.55036855 0.44963145) *
## 39) texture_mean< 2.892591 21 2 B (0.09523810 0.90476190)
## 78) smoothness_worst>=-1.459092 2 0 M (1.00000000 0.00000000) *
## 79) smoothness_worst< -1.459092 19 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.481821 171 70 B (0.40935673 0.59064327)
## 10) texture_mean< 2.760642 60 21 M (0.65000000 0.35000000)
## 20) smoothness_mean>=-2.360495 50 11 M (0.78000000 0.22000000)
## 40) compactness_se>=-3.943187 47 8 M (0.82978723 0.17021277)
## 80) symmetry_worst< -1.461208 42 5 M (0.88095238 0.11904762) *
## 81) symmetry_worst>=-1.461208 5 2 B (0.40000000 0.60000000) *
## 41) compactness_se< -3.943187 3 0 B (0.00000000 1.00000000) *
## 21) smoothness_mean< -2.360495 10 0 B (0.00000000 1.00000000) *
## 11) texture_mean>=2.760642 111 31 B (0.27927928 0.72072072)
## 22) compactness_se< -4.160164 8 0 M (1.00000000 0.00000000) *
## 23) compactness_se>=-4.160164 103 23 B (0.22330097 0.77669903)
## 46) texture_worst< 4.034664 5 0 M (1.00000000 0.00000000) *
## 47) texture_worst>=4.034664 98 18 B (0.18367347 0.81632653)
## 94) compactness_se>=-3.294139 11 4 M (0.63636364 0.36363636) *
## 95) compactness_se< -3.294139 87 11 B (0.12643678 0.87356322) *
## 3) compactness_se< -4.198706 213 83 B (0.38967136 0.61032864)
## 6) smoothness_mean< -2.3007 176 81 B (0.46022727 0.53977273)
## 12) texture_mean< 3.217018 147 69 M (0.53061224 0.46938776)
## 24) texture_mean>=2.960617 85 26 M (0.69411765 0.30588235)
## 48) texture_worst< 4.984637 60 10 M (0.83333333 0.16666667)
## 96) symmetry_worst>=-2.046832 56 6 M (0.89285714 0.10714286) *
## 97) symmetry_worst< -2.046832 4 0 B (0.00000000 1.00000000) *
## 49) texture_worst>=4.984637 25 9 B (0.36000000 0.64000000)
## 98) texture_mean>=3.12836 9 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.12836 16 0 B (0.00000000 1.00000000) *
## 25) texture_mean< 2.960617 62 19 B (0.30645161 0.69354839)
## 50) compactness_se< -4.327955 41 19 B (0.46341463 0.53658537)
## 100) compactness_se>=-4.356557 8 0 M (1.00000000 0.00000000) *
## 101) compactness_se< -4.356557 33 11 B (0.33333333 0.66666667) *
## 51) compactness_se>=-4.327955 21 0 B (0.00000000 1.00000000) *
## 13) texture_mean>=3.217018 29 3 B (0.10344828 0.89655172)
## 26) texture_mean>=3.388429 3 0 M (1.00000000 0.00000000) *
## 27) texture_mean< 3.388429 26 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.3007 37 2 B (0.05405405 0.94594595)
## 14) smoothness_worst>=-1.435212 2 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst< -1.435212 35 0 B (0.00000000 1.00000000) *
##
## $trees[[78]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 393 B (0.43092105 0.56907895)
## 2) symmetry_worst>=-2.20425 838 378 B (0.45107399 0.54892601)
## 4) smoothness_mean>=-2.423454 584 289 B (0.49486301 0.50513699)
## 8) symmetry_worst< -1.925345 103 27 M (0.73786408 0.26213592)
## 16) symmetry_worst>=-1.966829 41 2 M (0.95121951 0.04878049)
## 32) texture_mean>=2.753964 40 1 M (0.97500000 0.02500000)
## 64) smoothness_mean< -2.225218 39 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.225218 1 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.753964 1 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.966829 62 25 M (0.59677419 0.40322581)
## 34) texture_worst>=4.85229 18 0 M (1.00000000 0.00000000) *
## 35) texture_worst< 4.85229 44 19 B (0.43181818 0.56818182)
## 70) smoothness_worst< -1.471555 34 15 M (0.55882353 0.44117647) *
## 71) smoothness_worst>=-1.471555 10 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.925345 481 213 B (0.44282744 0.55717256)
## 18) symmetry_worst>=-1.839419 421 205 B (0.48693587 0.51306413)
## 36) compactness_se>=-3.66733 169 63 M (0.62721893 0.37278107)
## 72) compactness_se< -3.494301 45 4 M (0.91111111 0.08888889) *
## 73) compactness_se>=-3.494301 124 59 M (0.52419355 0.47580645) *
## 37) compactness_se< -3.66733 252 99 B (0.39285714 0.60714286)
## 74) symmetry_worst< -1.749307 60 24 M (0.60000000 0.40000000) *
## 75) symmetry_worst>=-1.749307 192 63 B (0.32812500 0.67187500) *
## 19) symmetry_worst< -1.839419 60 8 B (0.13333333 0.86666667)
## 38) texture_worst>=4.927821 4 0 M (1.00000000 0.00000000) *
## 39) texture_worst< 4.927821 56 4 B (0.07142857 0.92857143)
## 78) smoothness_mean< -2.352223 8 4 M (0.50000000 0.50000000) *
## 79) smoothness_mean>=-2.352223 48 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.423454 254 89 B (0.35039370 0.64960630)
## 10) smoothness_mean< -2.454106 161 76 B (0.47204969 0.52795031)
## 20) symmetry_worst>=-1.54778 27 2 M (0.92592593 0.07407407)
## 40) texture_mean>=2.91613 25 0 M (1.00000000 0.00000000) *
## 41) texture_mean< 2.91613 2 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.54778 134 51 B (0.38059701 0.61940299)
## 42) symmetry_worst< -1.617873 111 51 B (0.45945946 0.54054054)
## 84) symmetry_worst>=-1.844742 63 20 M (0.68253968 0.31746032) *
## 85) symmetry_worst< -1.844742 48 8 B (0.16666667 0.83333333) *
## 43) symmetry_worst>=-1.617873 23 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.454106 93 13 B (0.13978495 0.86021505)
## 22) texture_worst< 4.536807 15 7 M (0.53333333 0.46666667)
## 44) smoothness_worst< -1.595541 8 0 M (1.00000000 0.00000000) *
## 45) smoothness_worst>=-1.595541 7 0 B (0.00000000 1.00000000) *
## 23) texture_worst>=4.536807 78 5 B (0.06410256 0.93589744)
## 46) symmetry_worst< -1.993222 13 5 B (0.38461538 0.61538462)
## 92) smoothness_worst>=-1.525709 4 0 M (1.00000000 0.00000000) *
## 93) smoothness_worst< -1.525709 9 1 B (0.11111111 0.88888889) *
## 47) symmetry_worst>=-1.993222 65 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.20425 74 15 B (0.20270270 0.79729730)
## 6) compactness_se>=-3.487878 16 6 M (0.62500000 0.37500000)
## 12) compactness_se< -3.248462 11 1 M (0.90909091 0.09090909)
## 24) texture_mean>=2.822066 10 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.822066 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se>=-3.248462 5 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.487878 58 5 B (0.08620690 0.91379310)
## 14) compactness_se< -4.480041 8 3 M (0.62500000 0.37500000)
## 28) smoothness_mean< -2.271294 5 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.271294 3 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.480041 50 0 B (0.00000000 1.00000000) *
##
## $trees[[79]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 423 B (0.46381579 0.53618421)
## 2) texture_mean>=2.963467 526 247 M (0.53041825 0.46958175)
## 4) symmetry_worst>=-1.067772 13 0 M (1.00000000 0.00000000) *
## 5) symmetry_worst< -1.067772 513 247 M (0.51851852 0.48148148)
## 10) smoothness_mean< -2.093138 500 234 M (0.53200000 0.46800000)
## 20) compactness_se>=-4.706178 489 223 M (0.54396728 0.45603272)
## 40) symmetry_worst< -1.132261 478 212 M (0.55648536 0.44351464)
## 80) symmetry_worst>=-1.41845 19 0 M (1.00000000 0.00000000) *
## 81) symmetry_worst< -1.41845 459 212 M (0.53812636 0.46187364) *
## 41) symmetry_worst>=-1.132261 11 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -4.706178 11 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean>=-2.093138 13 0 B (0.00000000 1.00000000) *
## 3) texture_mean< 2.963467 386 144 B (0.37305699 0.62694301)
## 6) smoothness_mean>=-2.333148 197 93 B (0.47208122 0.52791878)
## 12) smoothness_worst< -1.477389 111 46 M (0.58558559 0.41441441)
## 24) smoothness_worst>=-1.482701 27 1 M (0.96296296 0.03703704)
## 48) texture_mean< 2.893521 26 0 M (1.00000000 0.00000000) *
## 49) texture_mean>=2.893521 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.482701 84 39 B (0.46428571 0.53571429)
## 50) smoothness_worst< -1.567043 14 1 M (0.92857143 0.07142857)
## 100) smoothness_mean>=-2.310275 13 0 M (1.00000000 0.00000000) *
## 101) smoothness_mean< -2.310275 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.567043 70 26 B (0.37142857 0.62857143)
## 102) texture_worst>=4.522453 29 11 M (0.62068966 0.37931034) *
## 103) texture_worst< 4.522453 41 8 B (0.19512195 0.80487805) *
## 13) smoothness_worst>=-1.477389 86 28 B (0.32558140 0.67441860)
## 26) texture_mean>=2.934384 13 1 M (0.92307692 0.07692308)
## 52) texture_worst< 4.599229 12 0 M (1.00000000 0.00000000) *
## 53) texture_worst>=4.599229 1 0 B (0.00000000 1.00000000) *
## 27) texture_mean< 2.934384 73 16 B (0.21917808 0.78082192)
## 54) symmetry_worst>=-1.36527 15 6 M (0.60000000 0.40000000)
## 108) texture_mean>=2.706904 10 1 M (0.90000000 0.10000000) *
## 109) texture_mean< 2.706904 5 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.36527 58 7 B (0.12068966 0.87931034)
## 110) texture_mean< 2.515298 3 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.515298 55 4 B (0.07272727 0.92727273) *
## 7) smoothness_mean< -2.333148 189 51 B (0.26984127 0.73015873)
## 14) compactness_se< -4.650552 18 3 M (0.83333333 0.16666667)
## 28) smoothness_mean< -2.441817 15 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.441817 3 0 B (0.00000000 1.00000000) *
## 15) compactness_se>=-4.650552 171 36 B (0.21052632 0.78947368)
## 30) smoothness_worst>=-1.472307 22 10 M (0.54545455 0.45454545)
## 60) symmetry_worst>=-1.64088 13 2 M (0.84615385 0.15384615)
## 120) texture_mean>=2.735974 11 0 M (1.00000000 0.00000000) *
## 121) texture_mean< 2.735974 2 0 B (0.00000000 1.00000000) *
## 61) symmetry_worst< -1.64088 9 1 B (0.11111111 0.88888889)
## 122) smoothness_mean>=-2.363458 1 0 M (1.00000000 0.00000000) *
## 123) smoothness_mean< -2.363458 8 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst< -1.472307 149 24 B (0.16107383 0.83892617)
## 62) symmetry_worst< -1.692331 91 24 B (0.26373626 0.73626374)
## 124) symmetry_worst>=-1.815934 40 20 M (0.50000000 0.50000000) *
## 125) symmetry_worst< -1.815934 51 4 B (0.07843137 0.92156863) *
## 63) symmetry_worst>=-1.692331 58 0 B (0.00000000 1.00000000) *
##
## $trees[[80]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 367 B (0.40241228 0.59758772)
## 2) symmetry_worst>=-1.322543 38 10 M (0.73684211 0.26315789)
## 4) compactness_se< -2.646661 34 6 M (0.82352941 0.17647059)
## 8) smoothness_worst>=-1.497484 27 1 M (0.96296296 0.03703704)
## 16) texture_mean>=2.644674 26 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.644674 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.497484 7 2 B (0.28571429 0.71428571)
## 18) texture_mean>=3.126045 2 0 M (1.00000000 0.00000000) *
## 19) texture_mean< 3.126045 5 0 B (0.00000000 1.00000000) *
## 5) compactness_se>=-2.646661 4 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.322543 874 339 B (0.38787185 0.61212815)
## 6) compactness_se< -4.098353 231 112 B (0.48484848 0.51515152)
## 12) smoothness_mean< -2.291157 203 94 M (0.53694581 0.46305419)
## 24) smoothness_mean>=-2.368246 45 8 M (0.82222222 0.17777778)
## 48) symmetry_worst< -1.476085 41 4 M (0.90243902 0.09756098)
## 96) smoothness_mean< -2.299097 32 0 M (1.00000000 0.00000000) *
## 97) smoothness_mean>=-2.299097 9 4 M (0.55555556 0.44444444) *
## 49) symmetry_worst>=-1.476085 4 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.368246 158 72 B (0.45569620 0.54430380)
## 50) texture_worst< 4.984637 132 62 M (0.53030303 0.46969697)
## 100) texture_mean>=2.976294 49 9 M (0.81632653 0.18367347) *
## 101) texture_mean< 2.976294 83 30 B (0.36144578 0.63855422) *
## 51) texture_worst>=4.984637 26 2 B (0.07692308 0.92307692)
## 102) compactness_se>=-4.265617 1 0 M (1.00000000 0.00000000) *
## 103) compactness_se< -4.265617 25 1 B (0.04000000 0.96000000) *
## 13) smoothness_mean>=-2.291157 28 3 B (0.10714286 0.89285714)
## 26) texture_worst>=4.59101 6 3 M (0.50000000 0.50000000)
## 52) smoothness_mean>=-2.22149 3 0 M (1.00000000 0.00000000) *
## 53) smoothness_mean< -2.22149 3 0 B (0.00000000 1.00000000) *
## 27) texture_worst< 4.59101 22 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-4.098353 643 227 B (0.35303266 0.64696734)
## 14) compactness_se>=-4.022675 581 223 B (0.38382100 0.61617900)
## 28) smoothness_worst>=-1.499656 248 119 M (0.52016129 0.47983871)
## 56) smoothness_mean>=-2.288684 135 45 M (0.66666667 0.33333333)
## 112) texture_mean>=2.920399 94 17 M (0.81914894 0.18085106) *
## 113) texture_mean< 2.920399 41 13 B (0.31707317 0.68292683) *
## 57) smoothness_mean< -2.288684 113 39 B (0.34513274 0.65486726)
## 114) symmetry_worst>=-1.431268 9 0 M (1.00000000 0.00000000) *
## 115) symmetry_worst< -1.431268 104 30 B (0.28846154 0.71153846) *
## 29) smoothness_worst< -1.499656 333 94 B (0.28228228 0.71771772)
## 58) smoothness_worst< -1.515751 263 91 B (0.34600760 0.65399240)
## 116) compactness_se>=-3.738233 190 82 B (0.43157895 0.56842105) *
## 117) compactness_se< -3.738233 73 9 B (0.12328767 0.87671233) *
## 59) smoothness_worst>=-1.515751 70 3 B (0.04285714 0.95714286)
## 118) compactness_se< -3.450179 17 3 B (0.17647059 0.82352941) *
## 119) compactness_se>=-3.450179 53 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -4.022675 62 4 B (0.06451613 0.93548387)
## 30) texture_mean>=3.112668 4 0 M (1.00000000 0.00000000) *
## 31) texture_mean< 3.112668 58 0 B (0.00000000 1.00000000) *
##
## $trees[[81]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 402 B (0.44078947 0.55921053)
## 2) compactness_se>=-3.812868 475 237 B (0.49894737 0.50105263)
## 4) smoothness_worst< -1.400053 416 193 M (0.53605769 0.46394231)
## 8) smoothness_worst>=-1.450407 25 1 M (0.96000000 0.04000000)
## 16) smoothness_mean>=-2.420336 24 0 M (1.00000000 0.00000000) *
## 17) smoothness_mean< -2.420336 1 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.450407 391 192 M (0.50895141 0.49104859)
## 18) smoothness_worst< -1.532606 164 63 M (0.61585366 0.38414634)
## 36) smoothness_worst>=-1.586874 81 18 M (0.77777778 0.22222222)
## 72) texture_mean< 3.168177 68 9 M (0.86764706 0.13235294) *
## 73) texture_mean>=3.168177 13 4 B (0.30769231 0.69230769) *
## 37) smoothness_worst< -1.586874 83 38 B (0.45783133 0.54216867)
## 74) smoothness_worst< -1.59459 68 30 M (0.55882353 0.44117647) *
## 75) smoothness_worst>=-1.59459 15 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst>=-1.532606 227 98 B (0.43171806 0.56828194)
## 38) smoothness_mean< -2.453563 18 0 M (1.00000000 0.00000000) *
## 39) smoothness_mean>=-2.453563 209 80 B (0.38277512 0.61722488)
## 78) smoothness_mean>=-2.367658 161 76 B (0.47204969 0.52795031) *
## 79) smoothness_mean< -2.367658 48 4 B (0.08333333 0.91666667) *
## 5) smoothness_worst>=-1.400053 59 14 B (0.23728814 0.76271186)
## 10) smoothness_worst>=-1.395608 31 14 B (0.45161290 0.54838710)
## 20) smoothness_mean< -2.194024 8 0 M (1.00000000 0.00000000) *
## 21) smoothness_mean>=-2.194024 23 6 B (0.26086957 0.73913043)
## 42) symmetry_worst>=-1.596878 9 3 M (0.66666667 0.33333333)
## 84) texture_mean>=2.688296 6 0 M (1.00000000 0.00000000) *
## 85) texture_mean< 2.688296 3 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst< -1.596878 14 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.395608 28 0 B (0.00000000 1.00000000) *
## 3) compactness_se< -3.812868 437 165 B (0.37757437 0.62242563)
## 6) compactness_se< -3.867535 394 163 B (0.41370558 0.58629442)
## 12) compactness_se>=-3.883925 24 1 M (0.95833333 0.04166667)
## 24) texture_mean>=2.689116 23 0 M (1.00000000 0.00000000) *
## 25) texture_mean< 2.689116 1 0 B (0.00000000 1.00000000) *
## 13) compactness_se< -3.883925 370 140 B (0.37837838 0.62162162)
## 26) texture_mean>=2.803607 341 140 B (0.41055718 0.58944282)
## 52) compactness_se< -3.935037 300 135 B (0.45000000 0.55000000)
## 104) compactness_se>=-3.977364 18 1 M (0.94444444 0.05555556) *
## 105) compactness_se< -3.977364 282 118 B (0.41843972 0.58156028) *
## 53) compactness_se>=-3.935037 41 5 B (0.12195122 0.87804878)
## 106) smoothness_mean>=-2.240561 4 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.240561 37 1 B (0.02702703 0.97297297) *
## 27) texture_mean< 2.803607 29 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.867535 43 2 B (0.04651163 0.95348837)
## 14) smoothness_worst>=-1.464982 4 2 M (0.50000000 0.50000000)
## 28) texture_mean< 2.90276 2 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=2.90276 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst< -1.464982 39 0 B (0.00000000 1.00000000) *
##
## $trees[[82]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 434 M (0.52412281 0.47587719)
## 2) symmetry_worst>=-1.641484 356 133 M (0.62640449 0.37359551)
## 4) smoothness_mean>=-2.320393 169 40 M (0.76331361 0.23668639)
## 8) compactness_se< -2.780114 162 33 M (0.79629630 0.20370370)
## 16) compactness_se>=-4.463708 157 28 M (0.82165605 0.17834395)
## 32) smoothness_mean< -2.301086 34 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.301086 123 28 M (0.77235772 0.22764228)
## 66) smoothness_mean>=-2.296604 118 23 M (0.80508475 0.19491525) *
## 67) smoothness_mean< -2.296604 5 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.463708 5 0 B (0.00000000 1.00000000) *
## 9) compactness_se>=-2.780114 7 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean< -2.320393 187 93 M (0.50267380 0.49732620)
## 10) compactness_se< -4.178455 62 14 M (0.77419355 0.22580645)
## 20) texture_mean< 3.110176 50 6 M (0.88000000 0.12000000)
## 40) texture_mean>=2.906784 47 3 M (0.93617021 0.06382979)
## 80) texture_worst>=4.340304 46 2 M (0.95652174 0.04347826) *
## 81) texture_worst< 4.340304 1 0 B (0.00000000 1.00000000) *
## 41) texture_mean< 2.906784 3 0 B (0.00000000 1.00000000) *
## 21) texture_mean>=3.110176 12 4 B (0.33333333 0.66666667)
## 42) texture_worst>=5.204837 4 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.204837 8 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.178455 125 46 B (0.36800000 0.63200000)
## 22) texture_worst>=4.993407 13 0 M (1.00000000 0.00000000) *
## 23) texture_worst< 4.993407 112 33 B (0.29464286 0.70535714)
## 46) symmetry_worst< -1.638169 14 1 M (0.92857143 0.07142857)
## 92) texture_mean>=2.7241 13 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.7241 1 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst>=-1.638169 98 20 B (0.20408163 0.79591837)
## 94) symmetry_worst>=-1.429489 19 8 M (0.57894737 0.42105263) *
## 95) symmetry_worst< -1.429489 79 9 B (0.11392405 0.88607595) *
## 3) symmetry_worst< -1.641484 556 255 B (0.45863309 0.54136691)
## 6) smoothness_mean< -2.237735 479 238 M (0.50313152 0.49686848)
## 12) smoothness_mean>=-2.257137 21 1 M (0.95238095 0.04761905)
## 24) compactness_se>=-3.898257 20 0 M (1.00000000 0.00000000) *
## 25) compactness_se< -3.898257 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.257137 458 221 B (0.48253275 0.51746725)
## 26) symmetry_worst< -1.750623 349 163 M (0.53295129 0.46704871)
## 52) texture_worst>=4.897936 81 23 M (0.71604938 0.28395062)
## 104) symmetry_worst>=-2.257286 75 17 M (0.77333333 0.22666667) *
## 105) symmetry_worst< -2.257286 6 0 B (0.00000000 1.00000000) *
## 53) texture_worst< 4.897936 268 128 B (0.47761194 0.52238806)
## 106) texture_worst< 4.751358 239 112 M (0.53138075 0.46861925) *
## 107) texture_worst>=4.751358 29 1 B (0.03448276 0.96551724) *
## 27) symmetry_worst>=-1.750623 109 35 B (0.32110092 0.67889908)
## 54) texture_mean>=2.955415 71 35 B (0.49295775 0.50704225)
## 108) symmetry_worst>=-1.716495 27 3 M (0.88888889 0.11111111) *
## 109) symmetry_worst< -1.716495 44 11 B (0.25000000 0.75000000) *
## 55) texture_mean< 2.955415 38 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.237735 77 14 B (0.18181818 0.81818182)
## 14) smoothness_worst< -1.56036 3 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst>=-1.56036 74 11 B (0.14864865 0.85135135)
## 30) texture_mean>=3.044046 12 6 M (0.50000000 0.50000000)
## 60) smoothness_mean< -2.120284 6 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean>=-2.120284 6 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.044046 62 5 B (0.08064516 0.91935484)
## 62) compactness_se>=-3.011681 2 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -3.011681 60 3 B (0.05000000 0.95000000)
## 126) compactness_se< -4.140724 10 3 B (0.30000000 0.70000000) *
## 127) compactness_se>=-4.140724 50 0 B (0.00000000 1.00000000) *
##
## $trees[[83]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 419 B (0.45942982 0.54057018)
## 2) symmetry_worst>=-1.658814 361 161 M (0.55401662 0.44598338)
## 4) texture_worst>=4.605737 211 68 M (0.67772512 0.32227488)
## 8) symmetry_worst< -1.606972 49 5 M (0.89795918 0.10204082)
## 16) texture_mean< 3.14185 43 0 M (1.00000000 0.00000000) *
## 17) texture_mean>=3.14185 6 1 B (0.16666667 0.83333333)
## 34) texture_mean>=3.244071 1 0 M (1.00000000 0.00000000) *
## 35) texture_mean< 3.244071 5 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.606972 162 63 M (0.61111111 0.38888889)
## 18) symmetry_worst>=-1.591238 151 52 M (0.65562914 0.34437086)
## 36) texture_worst>=4.993407 30 2 M (0.93333333 0.06666667)
## 72) compactness_se>=-4.507761 28 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.507761 2 0 B (0.00000000 1.00000000) *
## 37) texture_worst< 4.993407 121 50 M (0.58677686 0.41322314)
## 74) compactness_se< -3.768789 65 15 M (0.76923077 0.23076923) *
## 75) compactness_se>=-3.768789 56 21 B (0.37500000 0.62500000) *
## 19) symmetry_worst< -1.591238 11 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.605737 150 57 B (0.38000000 0.62000000)
## 10) smoothness_mean>=-2.171581 28 4 M (0.85714286 0.14285714)
## 20) compactness_se>=-3.95959 26 2 M (0.92307692 0.07692308)
## 40) smoothness_worst< -1.333822 23 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst>=-1.333822 3 1 B (0.33333333 0.66666667)
## 82) texture_mean>=2.688296 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 2.688296 2 0 B (0.00000000 1.00000000) *
## 21) compactness_se< -3.95959 2 0 B (0.00000000 1.00000000) *
## 11) smoothness_mean< -2.171581 122 33 B (0.27049180 0.72950820)
## 22) smoothness_worst>=-1.472112 43 21 B (0.48837209 0.51162791)
## 44) symmetry_worst< -1.397194 17 2 M (0.88235294 0.11764706)
## 88) texture_worst>=4.110502 15 0 M (1.00000000 0.00000000) *
## 89) texture_worst< 4.110502 2 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst>=-1.397194 26 6 B (0.23076923 0.76923077)
## 90) texture_worst< 4.074625 5 0 M (1.00000000 0.00000000) *
## 91) texture_worst>=4.074625 21 1 B (0.04761905 0.95238095) *
## 23) smoothness_worst< -1.472112 79 12 B (0.15189873 0.84810127)
## 46) texture_mean>=2.975525 9 2 M (0.77777778 0.22222222)
## 92) smoothness_mean< -2.275789 7 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean>=-2.275789 2 0 B (0.00000000 1.00000000) *
## 47) texture_mean< 2.975525 70 5 B (0.07142857 0.92857143)
## 94) smoothness_worst>=-1.496237 21 5 B (0.23809524 0.76190476) *
## 95) smoothness_worst< -1.496237 49 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.658814 551 219 B (0.39745917 0.60254083)
## 6) symmetry_worst< -1.681676 525 219 B (0.41714286 0.58285714)
## 12) smoothness_worst< -1.476214 417 192 B (0.46043165 0.53956835)
## 24) smoothness_worst>=-1.482699 31 2 M (0.93548387 0.06451613)
## 48) compactness_se>=-3.967101 27 0 M (1.00000000 0.00000000) *
## 49) compactness_se< -3.967101 4 2 M (0.50000000 0.50000000)
## 98) texture_mean>=2.981733 2 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.981733 2 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.482699 386 163 B (0.42227979 0.57772021)
## 50) symmetry_worst>=-1.815934 122 50 M (0.59016393 0.40983607)
## 100) smoothness_worst< -1.484675 104 32 M (0.69230769 0.30769231) *
## 101) smoothness_worst>=-1.484675 18 0 B (0.00000000 1.00000000) *
## 51) symmetry_worst< -1.815934 264 91 B (0.34469697 0.65530303)
## 102) texture_worst>=4.624749 102 48 M (0.52941176 0.47058824) *
## 103) texture_worst< 4.624749 162 37 B (0.22839506 0.77160494) *
## 13) smoothness_worst>=-1.476214 108 27 B (0.25000000 0.75000000)
## 26) compactness_se>=-3.294139 11 3 M (0.72727273 0.27272727)
## 52) texture_mean< 3.23593 8 0 M (1.00000000 0.00000000) *
## 53) texture_mean>=3.23593 3 0 B (0.00000000 1.00000000) *
## 27) compactness_se< -3.294139 97 19 B (0.19587629 0.80412371)
## 54) texture_worst< 4.624204 40 14 B (0.35000000 0.65000000)
## 108) texture_worst>=4.373034 15 1 M (0.93333333 0.06666667) *
## 109) texture_worst< 4.373034 25 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.624204 57 5 B (0.08771930 0.91228070)
## 110) texture_mean>=3.207548 2 0 M (1.00000000 0.00000000) *
## 111) texture_mean< 3.207548 55 3 B (0.05454545 0.94545455) *
## 7) symmetry_worst>=-1.681676 26 0 B (0.00000000 1.00000000) *
##
## $trees[[84]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 413 B (0.45285088 0.54714912)
## 2) symmetry_worst>=-1.580867 257 111 M (0.56809339 0.43190661)
## 4) texture_worst>=4.477941 193 64 M (0.66839378 0.33160622)
## 8) symmetry_worst< -1.557842 25 0 M (1.00000000 0.00000000) *
## 9) symmetry_worst>=-1.557842 168 64 M (0.61904762 0.38095238)
## 18) symmetry_worst>=-1.549706 153 49 M (0.67973856 0.32026144)
## 36) smoothness_worst< -1.513087 58 8 M (0.86206897 0.13793103)
## 72) texture_mean>=2.904002 55 5 M (0.90909091 0.09090909) *
## 73) texture_mean< 2.904002 3 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst>=-1.513087 95 41 M (0.56842105 0.43157895)
## 74) smoothness_mean>=-2.277448 38 5 M (0.86842105 0.13157895) *
## 75) smoothness_mean< -2.277448 57 21 B (0.36842105 0.63157895) *
## 19) symmetry_worst< -1.549706 15 0 B (0.00000000 1.00000000) *
## 5) texture_worst< 4.477941 64 17 B (0.26562500 0.73437500)
## 10) smoothness_worst>=-1.496237 36 17 B (0.47222222 0.52777778)
## 20) texture_mean>=2.803301 13 1 M (0.92307692 0.07692308)
## 40) compactness_se< -2.679301 12 0 M (1.00000000 0.00000000) *
## 41) compactness_se>=-2.679301 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.803301 23 5 B (0.21739130 0.78260870)
## 42) texture_mean< 2.531355 3 0 M (1.00000000 0.00000000) *
## 43) texture_mean>=2.531355 20 2 B (0.10000000 0.90000000)
## 86) compactness_se>=-3.1317 2 0 M (1.00000000 0.00000000) *
## 87) compactness_se< -3.1317 18 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.496237 28 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -1.580867 655 267 B (0.40763359 0.59236641)
## 6) texture_mean>=3.431382 14 0 M (1.00000000 0.00000000) *
## 7) texture_mean< 3.431382 641 253 B (0.39469579 0.60530421)
## 14) texture_mean< 3.21023 557 236 B (0.42369838 0.57630162)
## 28) texture_worst>=5.016194 22 3 M (0.86363636 0.13636364)
## 56) texture_worst< 5.280287 19 0 M (1.00000000 0.00000000) *
## 57) texture_worst>=5.280287 3 0 B (0.00000000 1.00000000) *
## 29) texture_worst< 5.016194 535 217 B (0.40560748 0.59439252)
## 58) compactness_se< -3.391153 449 199 B (0.44320713 0.55679287)
## 116) compactness_se>=-3.772915 172 69 M (0.59883721 0.40116279) *
## 117) compactness_se< -3.772915 277 96 B (0.34657040 0.65342960) *
## 59) compactness_se>=-3.391153 86 18 B (0.20930233 0.79069767)
## 118) texture_mean>=3.038537 26 9 M (0.65384615 0.34615385) *
## 119) texture_mean< 3.038537 60 1 B (0.01666667 0.98333333) *
## 15) texture_mean>=3.21023 84 17 B (0.20238095 0.79761905)
## 30) symmetry_worst>=-1.709835 5 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst< -1.709835 79 12 B (0.15189873 0.84810127)
## 62) compactness_se>=-3.424051 5 1 M (0.80000000 0.20000000)
## 124) smoothness_mean< -2.457972 4 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.457972 1 0 B (0.00000000 1.00000000) *
## 63) compactness_se< -3.424051 74 8 B (0.10810811 0.89189189)
## 126) smoothness_worst>=-1.435634 2 0 M (1.00000000 0.00000000) *
## 127) smoothness_worst< -1.435634 72 6 B (0.08333333 0.91666667) *
##
## $trees[[85]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 442 B (0.48464912 0.51535088)
## 2) smoothness_mean>=-2.392182 553 246 M (0.55515371 0.44484629)
## 4) smoothness_mean< -2.38347 31 0 M (1.00000000 0.00000000) *
## 5) smoothness_mean>=-2.38347 522 246 M (0.52873563 0.47126437)
## 10) smoothness_mean>=-2.380331 509 233 M (0.54223969 0.45776031)
## 20) smoothness_worst< -1.562856 50 9 M (0.82000000 0.18000000)
## 40) smoothness_worst>=-1.574324 18 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.574324 32 9 M (0.71875000 0.28125000)
## 82) smoothness_mean>=-2.337942 24 3 M (0.87500000 0.12500000) *
## 83) smoothness_mean< -2.337942 8 2 B (0.25000000 0.75000000) *
## 21) smoothness_worst>=-1.562856 459 224 M (0.51198257 0.48801743)
## 42) symmetry_worst>=-2.151948 422 192 M (0.54502370 0.45497630)
## 84) compactness_se>=-4.50262 406 176 M (0.56650246 0.43349754) *
## 85) compactness_se< -4.50262 16 0 B (0.00000000 1.00000000) *
## 43) symmetry_worst< -2.151948 37 5 B (0.13513514 0.86486486)
## 86) compactness_se>=-3.382349 4 1 M (0.75000000 0.25000000) *
## 87) compactness_se< -3.382349 33 2 B (0.06060606 0.93939394) *
## 11) smoothness_mean< -2.380331 13 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.392182 359 135 B (0.37604457 0.62395543)
## 6) compactness_se< -3.941776 156 72 M (0.53846154 0.46153846)
## 12) smoothness_mean>=-2.422045 44 6 M (0.86363636 0.13636364)
## 24) smoothness_mean< -2.399979 39 1 M (0.97435897 0.02564103)
## 48) symmetry_worst< -1.677281 33 0 M (1.00000000 0.00000000) *
## 49) symmetry_worst>=-1.677281 6 1 M (0.83333333 0.16666667)
## 98) compactness_se>=-4.280953 5 0 M (1.00000000 0.00000000) *
## 99) compactness_se< -4.280953 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.399979 5 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.422045 112 46 B (0.41071429 0.58928571)
## 26) smoothness_mean< -2.443803 83 38 M (0.54216867 0.45783133)
## 52) symmetry_worst>=-1.562165 19 1 M (0.94736842 0.05263158)
## 104) texture_mean>=2.84952 18 0 M (1.00000000 0.00000000) *
## 105) texture_mean< 2.84952 1 0 B (0.00000000 1.00000000) *
## 53) symmetry_worst< -1.562165 64 27 B (0.42187500 0.57812500)
## 106) smoothness_worst>=-1.638322 52 25 M (0.51923077 0.48076923) *
## 107) smoothness_worst< -1.638322 12 0 B (0.00000000 1.00000000) *
## 27) smoothness_mean>=-2.443803 29 1 B (0.03448276 0.96551724)
## 54) smoothness_worst< -1.607486 2 1 M (0.50000000 0.50000000)
## 108) texture_mean>=2.884013 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 2.884013 1 0 B (0.00000000 1.00000000) *
## 55) smoothness_worst>=-1.607486 27 0 B (0.00000000 1.00000000) *
## 7) compactness_se>=-3.941776 203 51 B (0.25123153 0.74876847)
## 14) smoothness_mean< -2.461054 88 42 B (0.47727273 0.52272727)
## 28) smoothness_worst>=-1.556752 16 0 M (1.00000000 0.00000000) *
## 29) smoothness_worst< -1.556752 72 26 B (0.36111111 0.63888889)
## 58) symmetry_worst< -2.106078 18 2 M (0.88888889 0.11111111)
## 116) texture_mean>=3.076827 16 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 3.076827 2 0 B (0.00000000 1.00000000) *
## 59) symmetry_worst>=-2.106078 54 10 B (0.18518519 0.81481481)
## 118) symmetry_worst>=-1.816662 30 10 B (0.33333333 0.66666667) *
## 119) symmetry_worst< -1.816662 24 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean>=-2.461054 115 9 B (0.07826087 0.92173913)
## 30) texture_mean< 2.788514 7 2 M (0.71428571 0.28571429)
## 60) texture_mean>=2.735767 5 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.735767 2 0 B (0.00000000 1.00000000) *
## 31) texture_mean>=2.788514 108 4 B (0.03703704 0.96296296)
## 62) symmetry_worst>=-1.783406 34 4 B (0.11764706 0.88235294)
## 124) symmetry_worst< -1.685469 4 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst>=-1.685469 30 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst< -1.783406 74 0 B (0.00000000 1.00000000) *
##
## $trees[[86]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 363 B (0.39802632 0.60197368)
## 2) compactness_se< -4.098353 266 127 M (0.52255639 0.47744361)
## 4) smoothness_mean< -2.299097 212 84 M (0.60377358 0.39622642)
## 8) smoothness_mean>=-2.426508 111 16 M (0.85585586 0.14414414)
## 16) texture_mean>=2.799406 108 13 M (0.87962963 0.12037037)
## 32) smoothness_worst>=-1.555906 80 3 M (0.96250000 0.03750000)
## 64) symmetry_worst>=-2.212871 78 1 M (0.98717949 0.01282051) *
## 65) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 33) smoothness_worst< -1.555906 28 10 M (0.64285714 0.35714286)
## 66) smoothness_worst< -1.567258 21 3 M (0.85714286 0.14285714) *
## 67) smoothness_worst>=-1.567258 7 0 B (0.00000000 1.00000000) *
## 17) texture_mean< 2.799406 3 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.426508 101 33 B (0.32673267 0.67326733)
## 18) symmetry_worst>=-1.695215 32 12 M (0.62500000 0.37500000)
## 36) smoothness_mean< -2.449649 24 4 M (0.83333333 0.16666667)
## 72) smoothness_worst< -1.549205 21 1 M (0.95238095 0.04761905) *
## 73) smoothness_worst>=-1.549205 3 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.449649 8 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.695215 69 13 B (0.18840580 0.81159420)
## 38) smoothness_worst>=-1.55307 15 7 M (0.53333333 0.46666667)
## 76) smoothness_mean< -2.440656 8 0 M (1.00000000 0.00000000) *
## 77) smoothness_mean>=-2.440656 7 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst< -1.55307 54 5 B (0.09259259 0.90740741)
## 78) texture_mean< 2.969886 16 5 B (0.31250000 0.68750000) *
## 79) texture_mean>=2.969886 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_mean>=-2.299097 54 11 B (0.20370370 0.79629630)
## 10) compactness_se>=-4.222363 27 11 B (0.40740741 0.59259259)
## 20) compactness_se< -4.178775 12 1 M (0.91666667 0.08333333)
## 40) smoothness_worst>=-1.49438 11 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.49438 1 0 B (0.00000000 1.00000000) *
## 21) compactness_se>=-4.178775 15 0 B (0.00000000 1.00000000) *
## 11) compactness_se< -4.222363 27 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-4.098353 646 224 B (0.34674923 0.65325077)
## 6) smoothness_mean>=-2.394871 470 190 B (0.40425532 0.59574468)
## 12) compactness_se>=-4.025757 425 190 B (0.44705882 0.55294118)
## 24) texture_worst>=4.895983 65 20 M (0.69230769 0.30769231)
## 48) symmetry_worst>=-2.207988 55 10 M (0.81818182 0.18181818)
## 96) texture_mean< 3.36829 49 4 M (0.91836735 0.08163265) *
## 97) texture_mean>=3.36829 6 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst< -2.207988 10 0 B (0.00000000 1.00000000) *
## 25) texture_worst< 4.895983 360 145 B (0.40277778 0.59722222)
## 50) texture_worst< 4.782287 302 136 B (0.45033113 0.54966887)
## 100) smoothness_mean< -2.366217 21 2 M (0.90476190 0.09523810) *
## 101) smoothness_mean>=-2.366217 281 117 B (0.41637011 0.58362989) *
## 51) texture_worst>=4.782287 58 9 B (0.15517241 0.84482759)
## 102) compactness_se>=-2.785754 7 0 M (1.00000000 0.00000000) *
## 103) compactness_se< -2.785754 51 2 B (0.03921569 0.96078431) *
## 13) compactness_se< -4.025757 45 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.394871 176 34 B (0.19318182 0.80681818)
## 14) symmetry_worst>=-1.466953 8 2 M (0.75000000 0.25000000)
## 28) texture_worst< 4.774321 5 0 M (1.00000000 0.00000000) *
## 29) texture_worst>=4.774321 3 1 B (0.33333333 0.66666667)
## 58) texture_mean>=3.044129 1 0 M (1.00000000 0.00000000) *
## 59) texture_mean< 3.044129 2 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.466953 168 28 B (0.16666667 0.83333333)
## 30) smoothness_worst< -1.598711 56 19 B (0.33928571 0.66071429)
## 60) smoothness_worst>=-1.653746 18 5 M (0.72222222 0.27777778)
## 120) compactness_se< -3.268044 14 1 M (0.92857143 0.07142857) *
## 121) compactness_se>=-3.268044 4 0 B (0.00000000 1.00000000) *
## 61) smoothness_worst< -1.653746 38 6 B (0.15789474 0.84210526)
## 122) compactness_se>=-2.979429 8 2 M (0.75000000 0.25000000) *
## 123) compactness_se< -2.979429 30 0 B (0.00000000 1.00000000) *
## 31) smoothness_worst>=-1.598711 112 9 B (0.08035714 0.91964286)
## 62) texture_worst>=5.19153 4 1 M (0.75000000 0.25000000)
## 124) smoothness_mean< -2.473552 3 0 M (1.00000000 0.00000000) *
## 125) smoothness_mean>=-2.473552 1 0 B (0.00000000 1.00000000) *
## 63) texture_worst< 5.19153 108 6 B (0.05555556 0.94444444)
## 126) texture_worst>=4.568716 42 6 B (0.14285714 0.85714286) *
## 127) texture_worst< 4.568716 66 0 B (0.00000000 1.00000000) *
##
## $trees[[87]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 451 M (0.50548246 0.49451754)
## 2) smoothness_mean>=-2.423454 682 305 M (0.55278592 0.44721408)
## 4) smoothness_worst< -1.4768 397 150 M (0.62216625 0.37783375)
## 8) smoothness_worst>=-1.482107 52 2 M (0.96153846 0.03846154)
## 16) texture_worst>=4.126187 50 0 M (1.00000000 0.00000000) *
## 17) texture_worst< 4.126187 2 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482107 345 148 M (0.57101449 0.42898551)
## 18) compactness_se< -4.100467 83 21 M (0.74698795 0.25301205)
## 36) smoothness_mean< -2.3007 75 13 M (0.82666667 0.17333333)
## 72) texture_mean>=2.809289 71 9 M (0.87323944 0.12676056) *
## 73) texture_mean< 2.809289 4 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.3007 8 0 B (0.00000000 1.00000000) *
## 19) compactness_se>=-4.100467 262 127 M (0.51526718 0.48473282)
## 38) symmetry_worst>=-1.835199 156 56 M (0.64102564 0.35897436)
## 76) texture_worst>=4.57172 78 14 M (0.82051282 0.17948718) *
## 77) texture_worst< 4.57172 78 36 B (0.46153846 0.53846154) *
## 39) symmetry_worst< -1.835199 106 35 B (0.33018868 0.66981132)
## 78) symmetry_worst< -2.923662 9 0 M (1.00000000 0.00000000) *
## 79) symmetry_worst>=-2.923662 97 26 B (0.26804124 0.73195876) *
## 5) smoothness_worst>=-1.4768 285 130 B (0.45614035 0.54385965)
## 10) smoothness_worst>=-1.473476 251 121 M (0.51792829 0.48207171)
## 20) symmetry_worst>=-1.721298 160 57 M (0.64375000 0.35625000)
## 40) compactness_se>=-4.04059 112 27 M (0.75892857 0.24107143)
## 80) smoothness_mean>=-2.359377 97 13 M (0.86597938 0.13402062) *
## 81) smoothness_mean< -2.359377 15 1 B (0.06666667 0.93333333) *
## 41) compactness_se< -4.04059 48 18 B (0.37500000 0.62500000)
## 82) smoothness_mean< -2.294648 21 3 M (0.85714286 0.14285714) *
## 83) smoothness_mean>=-2.294648 27 0 B (0.00000000 1.00000000) *
## 21) symmetry_worst< -1.721298 91 27 B (0.29670330 0.70329670)
## 42) texture_worst< 4.623467 23 6 M (0.73913043 0.26086957)
## 84) texture_mean>=2.84692 18 1 M (0.94444444 0.05555556) *
## 85) texture_mean< 2.84692 5 0 B (0.00000000 1.00000000) *
## 43) texture_worst>=4.623467 68 10 B (0.14705882 0.85294118)
## 86) symmetry_worst< -1.820896 18 8 M (0.55555556 0.44444444) *
## 87) symmetry_worst>=-1.820896 50 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473476 34 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.423454 230 84 B (0.36521739 0.63478261)
## 6) smoothness_mean< -2.441446 198 83 B (0.41919192 0.58080808)
## 12) symmetry_worst>=-1.54778 21 4 M (0.80952381 0.19047619)
## 24) smoothness_mean>=-2.487591 18 1 M (0.94444444 0.05555556)
## 48) texture_mean>=2.844831 17 0 M (1.00000000 0.00000000) *
## 49) texture_mean< 2.844831 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean< -2.487591 3 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst< -1.54778 177 66 B (0.37288136 0.62711864)
## 26) symmetry_worst< -1.750953 132 60 B (0.45454545 0.54545455)
## 52) symmetry_worst>=-1.868413 47 15 M (0.68085106 0.31914894)
## 104) texture_mean< 2.977229 32 4 M (0.87500000 0.12500000) *
## 105) texture_mean>=2.977229 15 4 B (0.26666667 0.73333333) *
## 53) symmetry_worst< -1.868413 85 28 B (0.32941176 0.67058824)
## 106) compactness_se>=-3.514597 44 20 M (0.54545455 0.45454545) *
## 107) compactness_se< -3.514597 41 4 B (0.09756098 0.90243902) *
## 27) symmetry_worst>=-1.750953 45 6 B (0.13333333 0.86666667)
## 54) texture_mean>=2.958874 17 6 B (0.35294118 0.64705882)
## 108) compactness_se< -4.196102 7 1 M (0.85714286 0.14285714) *
## 109) compactness_se>=-4.196102 10 0 B (0.00000000 1.00000000) *
## 55) texture_mean< 2.958874 28 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean>=-2.441446 32 1 B (0.03125000 0.96875000)
## 14) smoothness_worst< -1.607486 1 0 M (1.00000000 0.00000000) *
## 15) smoothness_worst>=-1.607486 31 0 B (0.00000000 1.00000000) *
##
## $trees[[88]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 452 M (0.50438596 0.49561404)
## 2) smoothness_mean>=-2.367658 551 234 M (0.57531760 0.42468240)
## 4) smoothness_worst< -1.476605 301 92 M (0.69435216 0.30564784)
## 8) smoothness_worst>=-1.499656 100 10 M (0.90000000 0.10000000)
## 16) compactness_se>=-3.907039 77 1 M (0.98701299 0.01298701)
## 32) smoothness_mean< -2.221522 67 0 M (1.00000000 0.00000000) *
## 33) smoothness_mean>=-2.221522 10 1 M (0.90000000 0.10000000)
## 66) smoothness_mean>=-2.204571 9 0 M (1.00000000 0.00000000) *
## 67) smoothness_mean< -2.204571 1 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -3.907039 23 9 M (0.60869565 0.39130435)
## 34) smoothness_mean< -2.262441 17 3 M (0.82352941 0.17647059)
## 68) compactness_se>=-4.224388 14 0 M (1.00000000 0.00000000) *
## 69) compactness_se< -4.224388 3 0 B (0.00000000 1.00000000) *
## 35) smoothness_mean>=-2.262441 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.499656 201 82 M (0.59203980 0.40796020)
## 18) symmetry_worst< -1.543306 183 64 M (0.65027322 0.34972678)
## 36) smoothness_mean< -2.313857 81 13 M (0.83950617 0.16049383)
## 72) compactness_se< -3.492659 63 4 M (0.93650794 0.06349206) *
## 73) compactness_se>=-3.492659 18 9 M (0.50000000 0.50000000) *
## 37) smoothness_mean>=-2.313857 102 51 M (0.50000000 0.50000000)
## 74) compactness_se>=-3.685572 60 18 M (0.70000000 0.30000000) *
## 75) compactness_se< -3.685572 42 9 B (0.21428571 0.78571429) *
## 19) symmetry_worst>=-1.543306 18 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.476605 250 108 B (0.43200000 0.56800000)
## 10) smoothness_worst>=-1.473672 221 108 B (0.48868778 0.51131222)
## 20) texture_worst< 4.94309 186 82 M (0.55913978 0.44086022)
## 40) smoothness_mean< -2.306533 57 9 M (0.84210526 0.15789474)
## 80) smoothness_mean>=-2.361754 48 0 M (1.00000000 0.00000000) *
## 81) smoothness_mean< -2.361754 9 0 B (0.00000000 1.00000000) *
## 41) smoothness_mean>=-2.306533 129 56 B (0.43410853 0.56589147)
## 82) texture_worst>=4.398698 92 42 M (0.54347826 0.45652174) *
## 83) texture_worst< 4.398698 37 6 B (0.16216216 0.83783784) *
## 21) texture_worst>=4.94309 35 4 B (0.11428571 0.88571429)
## 42) compactness_se>=-3.116694 3 0 M (1.00000000 0.00000000) *
## 43) compactness_se< -3.116694 32 1 B (0.03125000 0.96875000)
## 86) texture_mean>=3.237842 1 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.237842 31 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.473672 29 0 B (0.00000000 1.00000000) *
## 3) smoothness_mean< -2.367658 361 143 B (0.39612188 0.60387812)
## 6) texture_mean< 3.336125 333 142 B (0.42642643 0.57357357)
## 12) texture_worst>=4.975502 67 23 M (0.65671642 0.34328358)
## 24) compactness_se>=-4.706178 58 14 M (0.75862069 0.24137931)
## 48) symmetry_worst>=-2.145206 46 4 M (0.91304348 0.08695652)
## 96) symmetry_worst< -1.637827 35 0 M (1.00000000 0.00000000) *
## 97) symmetry_worst>=-1.637827 11 4 M (0.63636364 0.36363636) *
## 49) symmetry_worst< -2.145206 12 2 B (0.16666667 0.83333333)
## 98) texture_mean>=3.330945 2 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 3.330945 10 0 B (0.00000000 1.00000000) *
## 25) compactness_se< -4.706178 9 0 B (0.00000000 1.00000000) *
## 13) texture_worst< 4.975502 266 98 B (0.36842105 0.63157895)
## 26) smoothness_worst>=-1.604472 186 82 B (0.44086022 0.55913978)
## 52) symmetry_worst< -2.035676 22 3 M (0.86363636 0.13636364)
## 104) smoothness_worst< -1.540052 20 1 M (0.95000000 0.05000000) *
## 105) smoothness_worst>=-1.540052 2 0 B (0.00000000 1.00000000) *
## 53) symmetry_worst>=-2.035676 164 63 B (0.38414634 0.61585366)
## 106) texture_mean>=2.921008 115 54 B (0.46956522 0.53043478) *
## 107) texture_mean< 2.921008 49 9 B (0.18367347 0.81632653) *
## 27) smoothness_worst< -1.604472 80 16 B (0.20000000 0.80000000)
## 54) symmetry_worst>=-1.777195 22 10 B (0.45454545 0.54545455)
## 108) texture_mean>=2.939162 13 3 M (0.76923077 0.23076923) *
## 109) texture_mean< 2.939162 9 0 B (0.00000000 1.00000000) *
## 55) symmetry_worst< -1.777195 58 6 B (0.10344828 0.89655172)
## 110) compactness_se>=-2.951614 10 4 M (0.60000000 0.40000000) *
## 111) compactness_se< -2.951614 48 0 B (0.00000000 1.00000000) *
## 7) texture_mean>=3.336125 28 1 B (0.03571429 0.96428571)
## 14) texture_mean>=3.452615 1 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.452615 27 0 B (0.00000000 1.00000000) *
##
## $trees[[89]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 426 B (0.46710526 0.53289474)
## 2) smoothness_worst< -1.434076 775 387 M (0.50064516 0.49935484)
## 4) smoothness_mean>=-2.172878 31 0 M (1.00000000 0.00000000) *
## 5) smoothness_mean< -2.172878 744 357 B (0.47983871 0.52016129)
## 10) smoothness_worst>=-1.603315 627 302 M (0.51834131 0.48165869)
## 20) texture_worst>=4.579906 328 126 M (0.61585366 0.38414634)
## 40) texture_worst< 4.756552 150 32 M (0.78666667 0.21333333)
## 80) texture_mean>=3.055881 60 0 M (1.00000000 0.00000000) *
## 81) texture_mean< 3.055881 90 32 M (0.64444444 0.35555556) *
## 41) texture_worst>=4.756552 178 84 B (0.47191011 0.52808989)
## 82) smoothness_worst>=-1.504916 82 31 M (0.62195122 0.37804878) *
## 83) smoothness_worst< -1.504916 96 33 B (0.34375000 0.65625000) *
## 21) texture_worst< 4.579906 299 123 B (0.41137124 0.58862876)
## 42) texture_worst< 4.545141 253 118 B (0.46640316 0.53359684)
## 84) texture_worst>=4.522453 35 4 M (0.88571429 0.11428571) *
## 85) texture_worst< 4.522453 218 87 B (0.39908257 0.60091743) *
## 43) texture_worst>=4.545141 46 5 B (0.10869565 0.89130435)
## 86) texture_mean>=3.035431 5 0 M (1.00000000 0.00000000) *
## 87) texture_mean< 3.035431 41 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.603315 117 32 B (0.27350427 0.72649573)
## 22) symmetry_worst>=-1.550826 14 4 M (0.71428571 0.28571429)
## 44) smoothness_mean>=-2.43698 9 0 M (1.00000000 0.00000000) *
## 45) smoothness_mean< -2.43698 5 1 B (0.20000000 0.80000000)
## 90) symmetry_worst>=-1.211778 1 0 M (1.00000000 0.00000000) *
## 91) symmetry_worst< -1.211778 4 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.550826 103 22 B (0.21359223 0.78640777)
## 46) texture_mean< 2.966301 22 10 M (0.54545455 0.45454545)
## 92) texture_mean>=2.923842 16 4 M (0.75000000 0.25000000) *
## 93) texture_mean< 2.923842 6 0 B (0.00000000 1.00000000) *
## 47) texture_mean>=2.966301 81 10 B (0.12345679 0.87654321)
## 94) smoothness_worst< -1.720903 11 5 B (0.45454545 0.54545455) *
## 95) smoothness_worst>=-1.720903 70 5 B (0.07142857 0.92857143) *
## 3) smoothness_worst>=-1.434076 137 38 B (0.27737226 0.72262774)
## 6) symmetry_worst>=-1.270655 11 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.270655 126 27 B (0.21428571 0.78571429)
## 14) smoothness_mean>=-1.977294 8 1 M (0.87500000 0.12500000)
## 28) texture_mean>=2.649801 7 0 M (1.00000000 0.00000000) *
## 29) texture_mean< 2.649801 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -1.977294 118 20 B (0.16949153 0.83050847)
## 30) compactness_se>=-3.311998 12 4 M (0.66666667 0.33333333)
## 60) smoothness_mean>=-2.314128 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.314128 4 0 B (0.00000000 1.00000000) *
## 31) compactness_se< -3.311998 106 12 B (0.11320755 0.88679245)
## 62) smoothness_mean< -2.305218 9 4 M (0.55555556 0.44444444)
## 124) texture_mean>=3.075523 4 0 M (1.00000000 0.00000000) *
## 125) texture_mean< 3.075523 5 1 B (0.20000000 0.80000000) *
## 63) smoothness_mean>=-2.305218 97 7 B (0.07216495 0.92783505)
## 126) compactness_se< -3.475452 50 7 B (0.14000000 0.86000000) *
## 127) compactness_se>=-3.475452 47 0 B (0.00000000 1.00000000) *
##
## $trees[[90]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 436 B (0.47807018 0.52192982)
## 2) smoothness_worst< -1.434076 790 389 M (0.50759494 0.49240506)
## 4) smoothness_worst>=-1.603315 662 301 M (0.54531722 0.45468278)
## 8) smoothness_mean>=-2.477713 633 277 M (0.56240126 0.43759874)
## 16) texture_mean< 2.81481 83 19 M (0.77108434 0.22891566)
## 32) compactness_se>=-3.964431 76 12 M (0.84210526 0.15789474)
## 64) compactness_se< -3.340373 67 5 M (0.92537313 0.07462687) *
## 65) compactness_se>=-3.340373 9 2 B (0.22222222 0.77777778) *
## 33) compactness_se< -3.964431 7 0 B (0.00000000 1.00000000) *
## 17) texture_mean>=2.81481 550 258 M (0.53090909 0.46909091)
## 34) smoothness_worst< -1.59596 16 0 M (1.00000000 0.00000000) *
## 35) smoothness_worst>=-1.59596 534 258 M (0.51685393 0.48314607)
## 70) symmetry_worst>=-2.193154 475 212 M (0.55368421 0.44631579) *
## 71) symmetry_worst< -2.193154 59 13 B (0.22033898 0.77966102) *
## 9) smoothness_mean< -2.477713 29 5 B (0.17241379 0.82758621)
## 18) symmetry_worst< -2.155071 2 0 M (1.00000000 0.00000000) *
## 19) symmetry_worst>=-2.155071 27 3 B (0.11111111 0.88888889)
## 38) texture_worst>=5.057104 7 3 B (0.42857143 0.57142857)
## 76) smoothness_worst< -1.565575 3 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst>=-1.565575 4 0 B (0.00000000 1.00000000) *
## 39) texture_worst< 5.057104 20 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.603315 128 40 B (0.31250000 0.68750000)
## 10) smoothness_worst< -1.723213 12 1 M (0.91666667 0.08333333)
## 20) texture_mean>=3.026052 11 0 M (1.00000000 0.00000000) *
## 21) texture_mean< 3.026052 1 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst>=-1.723213 116 29 B (0.25000000 0.75000000)
## 22) compactness_se< -4.200032 44 21 M (0.52272727 0.47727273)
## 44) symmetry_worst>=-1.874628 31 9 M (0.70967742 0.29032258)
## 88) texture_worst< 4.998675 24 2 M (0.91666667 0.08333333) *
## 89) texture_worst>=4.998675 7 0 B (0.00000000 1.00000000) *
## 45) symmetry_worst< -1.874628 13 1 B (0.07692308 0.92307692)
## 90) texture_mean>=3.149769 1 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 3.149769 12 0 B (0.00000000 1.00000000) *
## 23) compactness_se>=-4.200032 72 6 B (0.08333333 0.91666667)
## 46) symmetry_worst>=-1.550826 5 1 M (0.80000000 0.20000000)
## 92) smoothness_mean>=-2.49225 4 0 M (1.00000000 0.00000000) *
## 93) smoothness_mean< -2.49225 1 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.550826 67 2 B (0.02985075 0.97014925)
## 94) texture_mean< 2.958884 10 2 B (0.20000000 0.80000000) *
## 95) texture_mean>=2.958884 57 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.434076 122 35 B (0.28688525 0.71311475)
## 6) symmetry_worst>=-1.232339 8 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.232339 114 27 B (0.23684211 0.76315789)
## 14) compactness_se>=-3.311998 12 4 M (0.66666667 0.33333333)
## 28) texture_mean>=2.701935 10 2 M (0.80000000 0.20000000)
## 56) smoothness_mean>=-2.314128 8 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.314128 2 0 B (0.00000000 1.00000000) *
## 29) texture_mean< 2.701935 2 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.311998 102 19 B (0.18627451 0.81372549)
## 30) smoothness_mean>=-1.977294 5 0 M (1.00000000 0.00000000) *
## 31) smoothness_mean< -1.977294 97 14 B (0.14432990 0.85567010)
## 62) compactness_se< -3.475452 54 14 B (0.25925926 0.74074074)
## 124) symmetry_worst< -1.534985 31 14 B (0.45161290 0.54838710) *
## 125) symmetry_worst>=-1.534985 23 0 B (0.00000000 1.00000000) *
## 63) compactness_se>=-3.475452 43 0 B (0.00000000 1.00000000) *
##
## $trees[[91]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 406 B (0.44517544 0.55482456)
## 2) texture_mean< 2.787462 98 29 M (0.70408163 0.29591837)
## 4) compactness_se>=-3.891799 89 20 M (0.77528090 0.22471910)
## 8) texture_worst>=4.056844 46 1 M (0.97826087 0.02173913)
## 16) texture_mean>=2.709047 45 0 M (1.00000000 0.00000000) *
## 17) texture_mean< 2.709047 1 0 B (0.00000000 1.00000000) *
## 9) texture_worst< 4.056844 43 19 M (0.55813953 0.44186047)
## 18) smoothness_worst>=-1.498451 26 5 M (0.80769231 0.19230769)
## 36) smoothness_mean< -2.060513 22 1 M (0.95454545 0.04545455)
## 72) texture_worst>=3.768766 21 0 M (1.00000000 0.00000000) *
## 73) texture_worst< 3.768766 1 0 B (0.00000000 1.00000000) *
## 37) smoothness_mean>=-2.060513 4 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.498451 17 3 B (0.17647059 0.82352941)
## 38) texture_mean>=2.758692 3 0 M (1.00000000 0.00000000) *
## 39) texture_mean< 2.758692 14 0 B (0.00000000 1.00000000) *
## 5) compactness_se< -3.891799 9 0 B (0.00000000 1.00000000) *
## 3) texture_mean>=2.787462 814 337 B (0.41400491 0.58599509)
## 6) symmetry_worst< -2.49184 20 1 M (0.95000000 0.05000000)
## 12) texture_mean< 3.276838 19 0 M (1.00000000 0.00000000) *
## 13) texture_mean>=3.276838 1 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst>=-2.49184 794 318 B (0.40050378 0.59949622)
## 14) symmetry_worst>=-1.327359 27 5 M (0.81481481 0.18518519)
## 28) smoothness_mean< -2.307926 18 0 M (1.00000000 0.00000000) *
## 29) smoothness_mean>=-2.307926 9 4 B (0.44444444 0.55555556)
## 58) smoothness_mean>=-2.288752 5 1 M (0.80000000 0.20000000)
## 116) compactness_se< -2.883911 4 0 M (1.00000000 0.00000000) *
## 117) compactness_se>=-2.883911 1 0 B (0.00000000 1.00000000) *
## 59) smoothness_mean< -2.288752 4 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.327359 767 296 B (0.38591917 0.61408083)
## 30) smoothness_mean< -2.093138 744 295 B (0.39650538 0.60349462)
## 60) smoothness_worst>=-1.460895 141 63 M (0.55319149 0.44680851)
## 120) compactness_se>=-4.032549 72 17 M (0.76388889 0.23611111) *
## 121) compactness_se< -4.032549 69 23 B (0.33333333 0.66666667) *
## 61) smoothness_worst< -1.460895 603 217 B (0.35986733 0.64013267)
## 122) smoothness_worst< -1.4768 529 210 B (0.39697543 0.60302457) *
## 123) smoothness_worst>=-1.4768 74 7 B (0.09459459 0.90540541) *
## 31) smoothness_mean>=-2.093138 23 1 B (0.04347826 0.95652174)
## 62) texture_mean< 2.94627 1 0 M (1.00000000 0.00000000) *
## 63) texture_mean>=2.94627 22 0 B (0.00000000 1.00000000) *
##
## $trees[[92]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 417 B (0.45723684 0.54276316)
## 2) smoothness_worst< -1.532606 360 158 M (0.56111111 0.43888889)
## 4) smoothness_worst>=-1.559144 117 29 M (0.75213675 0.24786325)
## 8) smoothness_mean>=-2.48706 111 23 M (0.79279279 0.20720721)
## 16) texture_mean>=2.862952 90 10 M (0.88888889 0.11111111)
## 32) symmetry_worst>=-2.201537 80 5 M (0.93750000 0.06250000)
## 64) compactness_se>=-4.694501 78 3 M (0.96153846 0.03846154) *
## 65) compactness_se< -4.694501 2 0 B (0.00000000 1.00000000) *
## 33) symmetry_worst< -2.201537 10 5 M (0.50000000 0.50000000)
## 66) smoothness_mean< -2.414006 5 0 M (1.00000000 0.00000000) *
## 67) smoothness_mean>=-2.414006 5 0 B (0.00000000 1.00000000) *
## 17) texture_mean< 2.862952 21 8 B (0.38095238 0.61904762)
## 34) smoothness_worst>=-1.547262 10 2 M (0.80000000 0.20000000)
## 68) compactness_se>=-4.272056 8 0 M (1.00000000 0.00000000) *
## 69) compactness_se< -4.272056 2 0 B (0.00000000 1.00000000) *
## 35) smoothness_worst< -1.547262 11 0 B (0.00000000 1.00000000) *
## 9) smoothness_mean< -2.48706 6 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.559144 243 114 B (0.46913580 0.53086420)
## 10) compactness_se< -4.579712 39 7 M (0.82051282 0.17948718)
## 20) compactness_se>=-4.740419 32 0 M (1.00000000 0.00000000) *
## 21) compactness_se< -4.740419 7 0 B (0.00000000 1.00000000) *
## 11) compactness_se>=-4.579712 204 82 B (0.40196078 0.59803922)
## 22) symmetry_worst>=-1.966444 138 66 M (0.52173913 0.47826087)
## 44) compactness_se>=-4.260936 96 34 M (0.64583333 0.35416667)
## 88) smoothness_worst< -1.568787 86 25 M (0.70930233 0.29069767) *
## 89) smoothness_worst>=-1.568787 10 1 B (0.10000000 0.90000000) *
## 45) compactness_se< -4.260936 42 10 B (0.23809524 0.76190476)
## 90) smoothness_worst< -1.61379 12 2 M (0.83333333 0.16666667) *
## 91) smoothness_worst>=-1.61379 30 0 B (0.00000000 1.00000000) *
## 23) symmetry_worst< -1.966444 66 10 B (0.15151515 0.84848485)
## 46) smoothness_worst< -1.694089 10 4 M (0.60000000 0.40000000)
## 92) texture_mean>=3.03091 7 1 M (0.85714286 0.14285714) *
## 93) texture_mean< 3.03091 3 0 B (0.00000000 1.00000000) *
## 47) smoothness_worst>=-1.694089 56 4 B (0.07142857 0.92857143)
## 94) compactness_se>=-2.674921 2 0 M (1.00000000 0.00000000) *
## 95) compactness_se< -2.674921 54 2 B (0.03703704 0.96296296) *
## 3) smoothness_worst>=-1.532606 552 215 B (0.38949275 0.61050725)
## 6) smoothness_mean< -2.235394 433 190 B (0.43879908 0.56120092)
## 12) texture_worst< 4.545516 132 47 M (0.64393939 0.35606061)
## 24) smoothness_worst>=-1.520499 119 34 M (0.71428571 0.28571429)
## 48) texture_mean>=2.892314 44 1 M (0.97727273 0.02272727)
## 96) compactness_se>=-3.950529 39 0 M (1.00000000 0.00000000) *
## 97) compactness_se< -3.950529 5 1 M (0.80000000 0.20000000) *
## 49) texture_mean< 2.892314 75 33 M (0.56000000 0.44000000)
## 98) smoothness_worst>=-1.496838 63 21 M (0.66666667 0.33333333) *
## 99) smoothness_worst< -1.496838 12 0 B (0.00000000 1.00000000) *
## 25) smoothness_worst< -1.520499 13 0 B (0.00000000 1.00000000) *
## 13) texture_worst>=4.545516 301 105 B (0.34883721 0.65116279)
## 26) texture_mean>=2.975018 245 99 B (0.40408163 0.59591837)
## 52) compactness_se< -3.500605 155 74 M (0.52258065 0.47741935)
## 104) texture_mean< 3.210432 102 30 M (0.70588235 0.29411765) *
## 105) texture_mean>=3.210432 53 9 B (0.16981132 0.83018868) *
## 53) compactness_se>=-3.500605 90 18 B (0.20000000 0.80000000)
## 106) smoothness_mean>=-2.281841 8 0 M (1.00000000 0.00000000) *
## 107) smoothness_mean< -2.281841 82 10 B (0.12195122 0.87804878) *
## 27) texture_mean< 2.975018 56 6 B (0.10714286 0.89285714)
## 54) symmetry_worst>=-1.420115 3 0 M (1.00000000 0.00000000) *
## 55) symmetry_worst< -1.420115 53 3 B (0.05660377 0.94339623)
## 110) texture_mean< 2.851854 2 0 M (1.00000000 0.00000000) *
## 111) texture_mean>=2.851854 51 1 B (0.01960784 0.98039216) *
## 7) smoothness_mean>=-2.235394 119 25 B (0.21008403 0.78991597)
## 14) smoothness_mean>=-2.07745 8 1 M (0.87500000 0.12500000)
## 28) symmetry_worst< -1.400188 7 0 M (1.00000000 0.00000000) *
## 29) symmetry_worst>=-1.400188 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_mean< -2.07745 111 18 B (0.16216216 0.83783784)
## 30) texture_mean>=3.044522 20 10 M (0.50000000 0.50000000)
## 60) texture_mean< 3.184212 10 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.184212 10 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.044522 91 8 B (0.08791209 0.91208791)
## 62) compactness_se< -3.492408 41 8 B (0.19512195 0.80487805)
## 124) compactness_se>=-3.664511 3 0 M (1.00000000 0.00000000) *
## 125) compactness_se< -3.664511 38 5 B (0.13157895 0.86842105) *
## 63) compactness_se>=-3.492408 50 0 B (0.00000000 1.00000000) *
##
## $trees[[93]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 440 B (0.48245614 0.51754386)
## 2) smoothness_worst< -1.4768 618 282 M (0.54368932 0.45631068)
## 4) smoothness_worst>=-1.604472 521 210 M (0.59692898 0.40307102)
## 8) smoothness_worst>=-1.482699 51 6 M (0.88235294 0.11764706)
## 16) compactness_se>=-3.894783 43 0 M (1.00000000 0.00000000) *
## 17) compactness_se< -3.894783 8 2 B (0.25000000 0.75000000)
## 34) smoothness_mean< -2.282367 2 0 M (1.00000000 0.00000000) *
## 35) smoothness_mean>=-2.282367 6 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.482699 470 204 M (0.56595745 0.43404255)
## 18) smoothness_worst< -1.484675 455 189 M (0.58461538 0.41538462)
## 36) texture_mean>=3.034949 171 51 M (0.70175439 0.29824561)
## 72) texture_worst< 4.797934 43 0 M (1.00000000 0.00000000) *
## 73) texture_worst>=4.797934 128 51 M (0.60156250 0.39843750) *
## 37) texture_mean< 3.034949 284 138 M (0.51408451 0.48591549)
## 74) symmetry_worst< -2.111279 36 5 M (0.86111111 0.13888889) *
## 75) symmetry_worst>=-2.111279 248 115 B (0.46370968 0.53629032) *
## 19) smoothness_worst>=-1.484675 15 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.604472 97 25 B (0.25773196 0.74226804)
## 10) symmetry_worst>=-1.550826 12 3 M (0.75000000 0.25000000)
## 20) texture_mean>=2.967432 10 1 M (0.90000000 0.10000000)
## 40) smoothness_mean>=-2.592204 9 0 M (1.00000000 0.00000000) *
## 41) smoothness_mean< -2.592204 1 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.967432 2 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.550826 85 16 B (0.18823529 0.81176471)
## 22) texture_mean< 3.086027 41 16 B (0.39024390 0.60975610)
## 44) texture_mean>=2.935975 27 11 M (0.59259259 0.40740741)
## 88) smoothness_mean< -2.484925 23 7 M (0.69565217 0.30434783) *
## 89) smoothness_mean>=-2.484925 4 0 B (0.00000000 1.00000000) *
## 45) texture_mean< 2.935975 14 0 B (0.00000000 1.00000000) *
## 23) texture_mean>=3.086027 44 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst>=-1.4768 294 104 B (0.35374150 0.64625850)
## 6) symmetry_worst>=-1.352813 27 5 M (0.81481481 0.18518519)
## 12) smoothness_mean>=-2.365259 24 2 M (0.91666667 0.08333333)
## 24) smoothness_mean< -2.003731 22 1 M (0.95454545 0.04545455)
## 48) compactness_se< -2.646661 20 0 M (1.00000000 0.00000000) *
## 49) compactness_se>=-2.646661 2 1 M (0.50000000 0.50000000)
## 98) texture_mean>=2.915767 1 0 M (1.00000000 0.00000000) *
## 99) texture_mean< 2.915767 1 0 B (0.00000000 1.00000000) *
## 25) smoothness_mean>=-2.003731 2 1 M (0.50000000 0.50000000)
## 50) texture_mean>=2.823221 1 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.823221 1 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean< -2.365259 3 0 B (0.00000000 1.00000000) *
## 7) symmetry_worst< -1.352813 267 82 B (0.30711610 0.69288390)
## 14) texture_worst< 4.624204 97 47 B (0.48453608 0.51546392)
## 28) texture_mean>=2.934384 36 2 M (0.94444444 0.05555556)
## 56) compactness_se>=-4.35833 34 0 M (1.00000000 0.00000000) *
## 57) compactness_se< -4.35833 2 0 B (0.00000000 1.00000000) *
## 29) texture_mean< 2.934384 61 13 B (0.21311475 0.78688525)
## 58) texture_mean< 2.754252 13 4 M (0.69230769 0.30769231)
## 116) texture_mean>=2.728421 7 0 M (1.00000000 0.00000000) *
## 117) texture_mean< 2.728421 6 2 B (0.33333333 0.66666667) *
## 59) texture_mean>=2.754252 48 4 B (0.08333333 0.91666667)
## 118) smoothness_worst>=-1.449274 14 4 B (0.28571429 0.71428571) *
## 119) smoothness_worst< -1.449274 34 0 B (0.00000000 1.00000000) *
## 15) texture_worst>=4.624204 170 35 B (0.20588235 0.79411765)
## 30) texture_worst>=4.824912 83 33 B (0.39759036 0.60240964)
## 60) symmetry_worst< -1.820896 11 0 M (1.00000000 0.00000000) *
## 61) symmetry_worst>=-1.820896 72 22 B (0.30555556 0.69444444)
## 122) symmetry_worst>=-1.655812 32 11 M (0.65625000 0.34375000) *
## 123) symmetry_worst< -1.655812 40 1 B (0.02500000 0.97500000) *
## 31) texture_worst< 4.824912 87 2 B (0.02298851 0.97701149)
## 62) compactness_se< -4.222024 1 0 M (1.00000000 0.00000000) *
## 63) compactness_se>=-4.222024 86 1 B (0.01162791 0.98837209)
## 126) compactness_se>=-3.370923 1 0 M (1.00000000 0.00000000) *
## 127) compactness_se< -3.370923 85 0 B (0.00000000 1.00000000) *
##
## $trees[[94]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) texture_worst< 4.642157 458 195 M (0.57423581 0.42576419)
## 4) compactness_se< -3.344528 362 133 M (0.63259669 0.36740331)
## 8) symmetry_worst< -1.692017 264 75 M (0.71590909 0.28409091)
## 16) symmetry_worst>=-1.815934 114 11 M (0.90350877 0.09649123)
## 32) compactness_se>=-4.45131 112 9 M (0.91964286 0.08035714)
## 64) smoothness_mean< -2.188699 111 8 M (0.92792793 0.07207207) *
## 65) smoothness_mean>=-2.188699 1 0 B (0.00000000 1.00000000) *
## 33) compactness_se< -4.45131 2 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst< -1.815934 150 64 M (0.57333333 0.42666667)
## 34) smoothness_mean>=-2.419122 121 40 M (0.66942149 0.33057851)
## 68) symmetry_worst< -1.824299 109 29 M (0.73394495 0.26605505) *
## 69) symmetry_worst>=-1.824299 12 1 B (0.08333333 0.91666667) *
## 35) smoothness_mean< -2.419122 29 5 B (0.17241379 0.82758621)
## 70) compactness_se>=-3.514597 10 5 M (0.50000000 0.50000000) *
## 71) compactness_se< -3.514597 19 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.692017 98 40 B (0.40816327 0.59183673)
## 18) smoothness_worst>=-1.451541 19 3 M (0.84210526 0.15789474)
## 36) smoothness_worst< -1.42057 13 0 M (1.00000000 0.00000000) *
## 37) smoothness_worst>=-1.42057 6 3 M (0.50000000 0.50000000)
## 74) texture_mean>=2.688296 3 0 M (1.00000000 0.00000000) *
## 75) texture_mean< 2.688296 3 0 B (0.00000000 1.00000000) *
## 19) smoothness_worst< -1.451541 79 24 B (0.30379747 0.69620253)
## 38) compactness_se< -4.681232 14 3 M (0.78571429 0.21428571)
## 76) texture_mean< 2.936149 11 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=2.936149 3 0 B (0.00000000 1.00000000) *
## 39) compactness_se>=-4.681232 65 13 B (0.20000000 0.80000000)
## 78) smoothness_mean>=-2.170258 6 0 M (1.00000000 0.00000000) *
## 79) smoothness_mean< -2.170258 59 7 B (0.11864407 0.88135593) *
## 5) compactness_se>=-3.344528 96 34 B (0.35416667 0.64583333)
## 10) symmetry_worst>=-1.001713 16 0 M (1.00000000 0.00000000) *
## 11) symmetry_worst< -1.001713 80 18 B (0.22500000 0.77500000)
## 22) texture_mean>=3.023554 9 1 M (0.88888889 0.11111111)
## 44) texture_worst< 4.544 8 0 M (1.00000000 0.00000000) *
## 45) texture_worst>=4.544 1 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.023554 71 10 B (0.14084507 0.85915493)
## 46) smoothness_worst>=-1.500893 29 10 B (0.34482759 0.65517241)
## 92) texture_mean>=2.915992 9 0 M (1.00000000 0.00000000) *
## 93) texture_mean< 2.915992 20 1 B (0.05000000 0.95000000) *
## 47) smoothness_worst< -1.500893 42 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.642157 454 170 B (0.37444934 0.62555066)
## 6) compactness_se>=-3.334337 80 29 M (0.63750000 0.36250000)
## 12) smoothness_mean>=-2.338127 34 0 M (1.00000000 0.00000000) *
## 13) smoothness_mean< -2.338127 46 17 B (0.36956522 0.63043478)
## 26) texture_worst>=4.993407 16 0 M (1.00000000 0.00000000) *
## 27) texture_worst< 4.993407 30 1 B (0.03333333 0.96666667)
## 54) texture_worst< 4.684099 4 1 B (0.25000000 0.75000000)
## 108) texture_mean>=3.079461 1 0 M (1.00000000 0.00000000) *
## 109) texture_mean< 3.079461 3 0 B (0.00000000 1.00000000) *
## 55) texture_worst>=4.684099 26 0 B (0.00000000 1.00000000) *
## 7) compactness_se< -3.334337 374 119 B (0.31818182 0.68181818)
## 14) smoothness_mean>=-2.403622 255 98 B (0.38431373 0.61568627)
## 28) smoothness_mean< -2.382712 33 3 M (0.90909091 0.09090909)
## 56) symmetry_worst>=-2.212871 31 1 M (0.96774194 0.03225806)
## 112) texture_mean>=2.920077 30 0 M (1.00000000 0.00000000) *
## 113) texture_mean< 2.920077 1 0 B (0.00000000 1.00000000) *
## 57) symmetry_worst< -2.212871 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.382712 222 68 B (0.30630631 0.69369369)
## 58) texture_worst>=4.818867 126 53 B (0.42063492 0.57936508)
## 116) symmetry_worst>=-1.71268 40 11 M (0.72500000 0.27500000) *
## 117) symmetry_worst< -1.71268 86 24 B (0.27906977 0.72093023) *
## 59) texture_worst< 4.818867 96 15 B (0.15625000 0.84375000)
## 118) smoothness_mean< -2.353585 9 2 M (0.77777778 0.22222222) *
## 119) smoothness_mean>=-2.353585 87 8 B (0.09195402 0.90804598) *
## 15) smoothness_mean< -2.403622 119 21 B (0.17647059 0.82352941)
## 30) smoothness_mean< -2.443746 66 21 B (0.31818182 0.68181818)
## 60) smoothness_mean>=-2.450864 8 0 M (1.00000000 0.00000000) *
## 61) smoothness_mean< -2.450864 58 13 B (0.22413793 0.77586207)
## 122) compactness_se< -4.620161 13 5 M (0.61538462 0.38461538) *
## 123) compactness_se>=-4.620161 45 5 B (0.11111111 0.88888889) *
## 31) smoothness_mean>=-2.443746 53 0 B (0.00000000 1.00000000) *
##
## $trees[[95]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 448 B (0.49122807 0.50877193)
## 2) smoothness_worst>=-1.609811 812 389 M (0.52093596 0.47906404)
## 4) smoothness_worst< -1.533657 271 99 M (0.63468635 0.36531365)
## 8) compactness_se>=-3.545992 67 9 M (0.86567164 0.13432836)
## 16) texture_worst>=4.411908 50 2 M (0.96000000 0.04000000)
## 32) symmetry_worst>=-1.956813 41 0 M (1.00000000 0.00000000) *
## 33) symmetry_worst< -1.956813 9 2 M (0.77777778 0.22222222)
## 66) texture_mean>=3.044039 7 0 M (1.00000000 0.00000000) *
## 67) texture_mean< 3.044039 2 0 B (0.00000000 1.00000000) *
## 17) texture_worst< 4.411908 17 7 M (0.58823529 0.41176471)
## 34) compactness_se< -3.464112 10 0 M (1.00000000 0.00000000) *
## 35) compactness_se>=-3.464112 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.545992 204 90 M (0.55882353 0.44117647)
## 18) smoothness_mean>=-2.501158 193 79 M (0.59067358 0.40932642)
## 36) symmetry_worst>=-2.063958 159 55 M (0.65408805 0.34591195)
## 72) texture_mean>=2.983598 74 8 M (0.89189189 0.10810811) *
## 73) texture_mean< 2.983598 85 38 B (0.44705882 0.55294118) *
## 37) symmetry_worst< -2.063958 34 10 B (0.29411765 0.70588235)
## 74) smoothness_worst< -1.594361 10 0 M (1.00000000 0.00000000) *
## 75) smoothness_worst>=-1.594361 24 0 B (0.00000000 1.00000000) *
## 19) smoothness_mean< -2.501158 11 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.533657 541 251 B (0.46395564 0.53604436)
## 10) smoothness_worst>=-1.52382 490 244 M (0.50204082 0.49795918)
## 20) compactness_se>=-4.547852 463 217 M (0.53131749 0.46868251)
## 40) symmetry_worst>=-1.529476 85 20 M (0.76470588 0.23529412)
## 80) smoothness_mean>=-2.343303 75 10 M (0.86666667 0.13333333) *
## 81) smoothness_mean< -2.343303 10 0 B (0.00000000 1.00000000) *
## 41) symmetry_worst< -1.529476 378 181 B (0.47883598 0.52116402)
## 82) smoothness_mean< -2.299091 187 73 M (0.60962567 0.39037433) *
## 83) smoothness_mean>=-2.299091 191 67 B (0.35078534 0.64921466) *
## 21) compactness_se< -4.547852 27 0 B (0.00000000 1.00000000) *
## 11) smoothness_worst< -1.52382 51 5 B (0.09803922 0.90196078)
## 22) texture_mean>=3.084198 13 5 B (0.38461538 0.61538462)
## 44) texture_worst< 4.870528 5 0 M (1.00000000 0.00000000) *
## 45) texture_worst>=4.870528 8 0 B (0.00000000 1.00000000) *
## 23) texture_mean< 3.084198 38 0 B (0.00000000 1.00000000) *
## 3) smoothness_worst< -1.609811 100 25 B (0.25000000 0.75000000)
## 6) symmetry_worst>=-1.550826 8 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst< -1.550826 92 17 B (0.18478261 0.81521739)
## 14) texture_mean< 3.086027 58 17 B (0.29310345 0.70689655)
## 28) texture_worst>=4.818554 6 0 M (1.00000000 0.00000000) *
## 29) texture_worst< 4.818554 52 11 B (0.21153846 0.78846154)
## 58) symmetry_worst>=-1.627715 5 0 M (1.00000000 0.00000000) *
## 59) symmetry_worst< -1.627715 47 6 B (0.12765957 0.87234043)
## 118) texture_mean>=3.078218 4 0 M (1.00000000 0.00000000) *
## 119) texture_mean< 3.078218 43 2 B (0.04651163 0.95348837) *
## 15) texture_mean>=3.086027 34 0 B (0.00000000 1.00000000) *
##
## $trees[[96]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 446 M (0.51096491 0.48903509)
## 2) texture_worst< 4.782287 617 268 M (0.56564019 0.43435981)
## 4) symmetry_worst>=-1.834844 434 145 M (0.66589862 0.33410138)
## 8) symmetry_worst< -1.69453 216 48 M (0.77777778 0.22222222)
## 16) smoothness_worst>=-1.587787 194 33 M (0.82989691 0.17010309)
## 32) texture_mean>=2.891759 106 5 M (0.95283019 0.04716981)
## 64) smoothness_mean>=-2.450833 103 2 M (0.98058252 0.01941748) *
## 65) smoothness_mean< -2.450833 3 0 B (0.00000000 1.00000000) *
## 33) texture_mean< 2.891759 88 28 M (0.68181818 0.31818182)
## 66) symmetry_worst< -1.801798 38 0 M (1.00000000 0.00000000) *
## 67) symmetry_worst>=-1.801798 50 22 B (0.44000000 0.56000000) *
## 17) smoothness_worst< -1.587787 22 7 B (0.31818182 0.68181818)
## 34) symmetry_worst>=-1.787851 9 2 M (0.77777778 0.22222222)
## 68) texture_mean>=2.935975 7 0 M (1.00000000 0.00000000) *
## 69) texture_mean< 2.935975 2 0 B (0.00000000 1.00000000) *
## 35) symmetry_worst< -1.787851 13 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst>=-1.69453 218 97 M (0.55504587 0.44495413)
## 18) symmetry_worst>=-1.656986 203 82 M (0.59605911 0.40394089)
## 36) texture_mean>=3.061712 40 6 M (0.85000000 0.15000000)
## 72) smoothness_worst>=-1.609426 34 0 M (1.00000000 0.00000000) *
## 73) smoothness_worst< -1.609426 6 0 B (0.00000000 1.00000000) *
## 37) texture_mean< 3.061712 163 76 M (0.53374233 0.46625767)
## 74) texture_mean< 3.04903 149 62 M (0.58389262 0.41610738) *
## 75) texture_mean>=3.04903 14 0 B (0.00000000 1.00000000) *
## 19) symmetry_worst< -1.656986 15 0 B (0.00000000 1.00000000) *
## 5) symmetry_worst< -1.834844 183 60 B (0.32786885 0.67213115)
## 10) smoothness_worst< -1.542557 65 29 M (0.55384615 0.44615385)
## 20) smoothness_worst>=-1.559148 19 0 M (1.00000000 0.00000000) *
## 21) smoothness_worst< -1.559148 46 17 B (0.36956522 0.63043478)
## 42) compactness_se>=-3.604909 14 4 M (0.71428571 0.28571429)
## 84) symmetry_worst< -1.953067 12 2 M (0.83333333 0.16666667) *
## 85) symmetry_worst>=-1.953067 2 0 B (0.00000000 1.00000000) *
## 43) compactness_se< -3.604909 32 7 B (0.21875000 0.78125000)
## 86) compactness_se< -4.563271 5 1 M (0.80000000 0.20000000) *
## 87) compactness_se>=-4.563271 27 3 B (0.11111111 0.88888889) *
## 11) smoothness_worst>=-1.542557 118 24 B (0.20338983 0.79661017)
## 22) smoothness_worst>=-1.497846 54 22 B (0.40740741 0.59259259)
## 44) smoothness_worst< -1.476215 20 4 M (0.80000000 0.20000000)
## 88) compactness_se>=-3.79429 14 0 M (1.00000000 0.00000000) *
## 89) compactness_se< -3.79429 6 2 B (0.33333333 0.66666667) *
## 45) smoothness_worst>=-1.476215 34 6 B (0.17647059 0.82352941)
## 90) smoothness_worst>=-1.424105 7 2 M (0.71428571 0.28571429) *
## 91) smoothness_worst< -1.424105 27 1 B (0.03703704 0.96296296) *
## 23) smoothness_worst< -1.497846 64 2 B (0.03125000 0.96875000)
## 46) texture_worst>=4.649493 2 0 M (1.00000000 0.00000000) *
## 47) texture_worst< 4.649493 62 0 B (0.00000000 1.00000000) *
## 3) texture_worst>=4.782287 295 117 B (0.39661017 0.60338983)
## 6) texture_worst>=4.911888 199 98 M (0.50753769 0.49246231)
## 12) symmetry_worst< -1.733593 104 27 M (0.74038462 0.25961538)
## 24) symmetry_worst>=-2.207988 85 11 M (0.87058824 0.12941176)
## 48) compactness_se>=-4.706178 81 7 M (0.91358025 0.08641975)
## 96) smoothness_mean< -2.140427 79 5 M (0.93670886 0.06329114) *
## 97) smoothness_mean>=-2.140427 2 0 B (0.00000000 1.00000000) *
## 49) compactness_se< -4.706178 4 0 B (0.00000000 1.00000000) *
## 25) symmetry_worst< -2.207988 19 3 B (0.15789474 0.84210526)
## 50) compactness_se>=-3.413706 3 0 M (1.00000000 0.00000000) *
## 51) compactness_se< -3.413706 16 0 B (0.00000000 1.00000000) *
## 13) symmetry_worst>=-1.733593 95 24 B (0.25263158 0.74736842)
## 26) smoothness_worst>=-1.426681 11 0 M (1.00000000 0.00000000) *
## 27) smoothness_worst< -1.426681 84 13 B (0.15476190 0.84523810)
## 54) texture_worst< 4.941163 3 0 M (1.00000000 0.00000000) *
## 55) texture_worst>=4.941163 81 10 B (0.12345679 0.87654321)
## 110) texture_worst>=5.003123 38 10 B (0.26315789 0.73684211) *
## 111) texture_worst< 5.003123 43 0 B (0.00000000 1.00000000) *
## 7) texture_worst< 4.911888 96 16 B (0.16666667 0.83333333)
## 14) compactness_se>=-3.601962 25 12 B (0.48000000 0.52000000)
## 28) smoothness_worst< -1.506135 11 1 M (0.90909091 0.09090909)
## 56) smoothness_mean>=-2.51419 10 0 M (1.00000000 0.00000000) *
## 57) smoothness_mean< -2.51419 1 0 B (0.00000000 1.00000000) *
## 29) smoothness_worst>=-1.506135 14 2 B (0.14285714 0.85714286)
## 58) smoothness_mean>=-2.272702 1 0 M (1.00000000 0.00000000) *
## 59) smoothness_mean< -2.272702 13 1 B (0.07692308 0.92307692)
## 118) compactness_se< -3.500605 1 0 M (1.00000000 0.00000000) *
## 119) compactness_se>=-3.500605 12 0 B (0.00000000 1.00000000) *
## 15) compactness_se< -3.601962 71 4 B (0.05633803 0.94366197)
## 30) symmetry_worst>=-1.35602 3 0 M (1.00000000 0.00000000) *
## 31) symmetry_worst< -1.35602 68 1 B (0.01470588 0.98529412)
## 62) smoothness_mean< -2.480592 4 1 B (0.25000000 0.75000000)
## 124) texture_mean< 3.136036 1 0 M (1.00000000 0.00000000) *
## 125) texture_mean>=3.136036 3 0 B (0.00000000 1.00000000) *
## 63) smoothness_mean>=-2.480592 64 0 B (0.00000000 1.00000000) *
##
## $trees[[97]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 410 B (0.44956140 0.55043860)
## 2) symmetry_worst>=-2.031981 769 371 B (0.48244473 0.51755527)
## 4) smoothness_worst< -1.4768 498 227 M (0.54417671 0.45582329)
## 8) smoothness_worst>=-1.482699 42 0 M (1.00000000 0.00000000) *
## 9) smoothness_worst< -1.482699 456 227 M (0.50219298 0.49780702)
## 18) smoothness_worst>=-1.584838 362 161 M (0.55524862 0.44475138)
## 36) symmetry_worst< -1.730674 175 56 M (0.68000000 0.32000000)
## 72) compactness_se< -3.93685 92 15 M (0.83695652 0.16304348) *
## 73) compactness_se>=-3.93685 83 41 M (0.50602410 0.49397590) *
## 37) symmetry_worst>=-1.730674 187 82 B (0.43850267 0.56149733)
## 74) compactness_se>=-3.681558 83 28 M (0.66265060 0.33734940) *
## 75) compactness_se< -3.681558 104 27 B (0.25961538 0.74038462) *
## 19) smoothness_worst< -1.584838 94 28 B (0.29787234 0.70212766)
## 38) smoothness_mean< -2.473852 52 25 B (0.48076923 0.51923077)
## 76) smoothness_mean>=-2.507153 26 5 M (0.80769231 0.19230769) *
## 77) smoothness_mean< -2.507153 26 4 B (0.15384615 0.84615385) *
## 39) smoothness_mean>=-2.473852 42 3 B (0.07142857 0.92857143)
## 78) symmetry_worst>=-1.550826 4 1 M (0.75000000 0.25000000) *
## 79) symmetry_worst< -1.550826 38 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst>=-1.4768 271 100 B (0.36900369 0.63099631)
## 10) smoothness_worst>=-1.472112 220 99 B (0.45000000 0.55000000)
## 20) symmetry_worst>=-1.75757 181 88 M (0.51381215 0.48618785)
## 40) compactness_se>=-4.032549 108 36 M (0.66666667 0.33333333)
## 80) texture_worst>=4.40818 62 7 M (0.88709677 0.11290323) *
## 81) texture_worst< 4.40818 46 17 B (0.36956522 0.63043478) *
## 41) compactness_se< -4.032549 73 21 B (0.28767123 0.71232877)
## 82) symmetry_worst< -1.743442 9 0 M (1.00000000 0.00000000) *
## 83) symmetry_worst>=-1.743442 64 12 B (0.18750000 0.81250000) *
## 21) symmetry_worst< -1.75757 39 6 B (0.15384615 0.84615385)
## 42) texture_worst>=5.041355 3 0 M (1.00000000 0.00000000) *
## 43) texture_worst< 5.041355 36 3 B (0.08333333 0.91666667)
## 86) compactness_se< -3.791636 5 2 B (0.40000000 0.60000000) *
## 87) compactness_se>=-3.791636 31 1 B (0.03225806 0.96774194) *
## 11) smoothness_worst< -1.472112 51 1 B (0.01960784 0.98039216)
## 22) texture_mean>=3.069079 1 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.069079 50 0 B (0.00000000 1.00000000) *
## 3) symmetry_worst< -2.031981 143 39 B (0.27272727 0.72727273)
## 6) symmetry_worst< -2.813177 9 0 M (1.00000000 0.00000000) *
## 7) symmetry_worst>=-2.813177 134 30 B (0.22388060 0.77611940)
## 14) smoothness_worst< -1.720903 6 2 M (0.66666667 0.33333333)
## 28) texture_mean< 3.103494 4 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.103494 2 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.720903 128 26 B (0.20312500 0.79687500)
## 30) smoothness_worst>=-1.448989 2 0 M (1.00000000 0.00000000) *
## 31) smoothness_worst< -1.448989 126 24 B (0.19047619 0.80952381)
## 62) symmetry_worst< -2.384404 10 5 M (0.50000000 0.50000000)
## 124) symmetry_worst>=-2.522371 5 0 M (1.00000000 0.00000000) *
## 125) symmetry_worst< -2.522371 5 0 B (0.00000000 1.00000000) *
## 63) symmetry_worst>=-2.384404 116 19 B (0.16379310 0.83620690)
## 126) smoothness_mean< -2.334592 72 18 B (0.25000000 0.75000000) *
## 127) smoothness_mean>=-2.334592 44 1 B (0.02272727 0.97727273) *
##
## $trees[[98]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 433 B (0.47478070 0.52521930)
## 2) compactness_se< -3.132769 817 408 M (0.50061200 0.49938800)
## 4) symmetry_worst< -1.503393 719 338 M (0.52990264 0.47009736)
## 8) compactness_se>=-3.439211 78 17 M (0.78205128 0.21794872)
## 16) smoothness_mean< -2.231223 69 9 M (0.86956522 0.13043478)
## 32) texture_worst>=4.40102 48 2 M (0.95833333 0.04166667)
## 64) smoothness_worst>=-1.579519 44 0 M (1.00000000 0.00000000) *
## 65) smoothness_worst< -1.579519 4 2 M (0.50000000 0.50000000) *
## 33) texture_worst< 4.40102 21 7 M (0.66666667 0.33333333)
## 66) compactness_se< -3.420409 14 0 M (1.00000000 0.00000000) *
## 67) compactness_se>=-3.420409 7 0 B (0.00000000 1.00000000) *
## 17) smoothness_mean>=-2.231223 9 1 B (0.11111111 0.88888889)
## 34) texture_mean>=3.004098 2 1 M (0.50000000 0.50000000)
## 68) texture_mean< 3.140897 1 0 M (1.00000000 0.00000000) *
## 69) texture_mean>=3.140897 1 0 B (0.00000000 1.00000000) *
## 35) texture_mean< 3.004098 7 0 B (0.00000000 1.00000000) *
## 9) compactness_se< -3.439211 641 320 B (0.49921997 0.50078003)
## 18) compactness_se< -3.492659 563 257 M (0.54351687 0.45648313)
## 36) smoothness_worst>=-1.424105 29 1 M (0.96551724 0.03448276)
## 72) compactness_se>=-4.089202 28 0 M (1.00000000 0.00000000) *
## 73) compactness_se< -4.089202 1 0 B (0.00000000 1.00000000) *
## 37) smoothness_worst< -1.424105 534 256 M (0.52059925 0.47940075)
## 74) texture_mean< 2.84432 95 24 M (0.74736842 0.25263158) *
## 75) texture_mean>=2.84432 439 207 B (0.47152620 0.52847380) *
## 19) compactness_se>=-3.492659 78 14 B (0.17948718 0.82051282)
## 38) smoothness_worst< -1.542472 15 5 M (0.66666667 0.33333333)
## 76) smoothness_worst>=-1.618016 10 0 M (1.00000000 0.00000000) *
## 77) smoothness_worst< -1.618016 5 0 B (0.00000000 1.00000000) *
## 39) smoothness_worst>=-1.542472 63 4 B (0.06349206 0.93650794)
## 78) texture_mean>=3.061712 5 2 M (0.60000000 0.40000000) *
## 79) texture_mean< 3.061712 58 1 B (0.01724138 0.98275862) *
## 5) symmetry_worst>=-1.503393 98 28 B (0.28571429 0.71428571)
## 10) symmetry_worst>=-1.322543 25 8 M (0.68000000 0.32000000)
## 20) texture_mean>=2.763873 21 4 M (0.80952381 0.19047619)
## 40) smoothness_worst>=-1.496291 16 0 M (1.00000000 0.00000000) *
## 41) smoothness_worst< -1.496291 5 1 B (0.20000000 0.80000000)
## 82) texture_mean>=3.158816 1 0 M (1.00000000 0.00000000) *
## 83) texture_mean< 3.158816 4 0 B (0.00000000 1.00000000) *
## 21) texture_mean< 2.763873 4 0 B (0.00000000 1.00000000) *
## 11) symmetry_worst< -1.322543 73 11 B (0.15068493 0.84931507)
## 22) smoothness_mean< -2.370743 20 9 B (0.45000000 0.55000000)
## 44) smoothness_mean>=-2.425324 7 0 M (1.00000000 0.00000000) *
## 45) smoothness_mean< -2.425324 13 2 B (0.15384615 0.84615385)
## 90) texture_mean>=2.97943 2 0 M (1.00000000 0.00000000) *
## 91) texture_mean< 2.97943 11 0 B (0.00000000 1.00000000) *
## 23) smoothness_mean>=-2.370743 53 2 B (0.03773585 0.96226415)
## 46) compactness_se>=-3.807621 12 2 B (0.16666667 0.83333333)
## 92) compactness_se< -3.597317 2 0 M (1.00000000 0.00000000) *
## 93) compactness_se>=-3.597317 10 0 B (0.00000000 1.00000000) *
## 47) compactness_se< -3.807621 41 0 B (0.00000000 1.00000000) *
## 3) compactness_se>=-3.132769 95 24 B (0.25263158 0.74736842)
## 6) smoothness_mean>=-2.291354 39 17 M (0.56410256 0.43589744)
## 12) smoothness_worst>=-1.502935 29 7 M (0.75862069 0.24137931)
## 24) compactness_se< -2.552001 21 0 M (1.00000000 0.00000000) *
## 25) compactness_se>=-2.552001 8 1 B (0.12500000 0.87500000)
## 50) texture_mean>=2.929061 1 0 M (1.00000000 0.00000000) *
## 51) texture_mean< 2.929061 7 0 B (0.00000000 1.00000000) *
## 13) smoothness_worst< -1.502935 10 0 B (0.00000000 1.00000000) *
## 7) smoothness_mean< -2.291354 56 2 B (0.03571429 0.96428571)
## 14) smoothness_worst< -1.720903 2 1 M (0.50000000 0.50000000)
## 28) texture_mean< 3.103494 1 0 M (1.00000000 0.00000000) *
## 29) texture_mean>=3.103494 1 0 B (0.00000000 1.00000000) *
## 15) smoothness_worst>=-1.720903 54 1 B (0.01851852 0.98148148)
## 30) texture_mean>=3.083423 15 1 B (0.06666667 0.93333333)
## 60) texture_mean< 3.109209 1 0 M (1.00000000 0.00000000) *
## 61) texture_mean>=3.109209 14 0 B (0.00000000 1.00000000) *
## 31) texture_mean< 3.083423 39 0 B (0.00000000 1.00000000) *
##
## $trees[[99]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 430 M (0.52850877 0.47149123)
## 2) texture_worst>=4.580648 508 204 M (0.59842520 0.40157480)
## 4) texture_worst< 4.642157 98 12 M (0.87755102 0.12244898)
## 8) smoothness_worst>=-1.594449 91 6 M (0.93406593 0.06593407)
## 16) compactness_se>=-4.694501 88 3 M (0.96590909 0.03409091)
## 32) symmetry_worst>=-2.477165 86 1 M (0.98837209 0.01162791)
## 64) smoothness_mean< -2.227061 65 0 M (1.00000000 0.00000000) *
## 65) smoothness_mean>=-2.227061 21 1 M (0.95238095 0.04761905) *
## 33) symmetry_worst< -2.477165 2 0 B (0.00000000 1.00000000) *
## 17) compactness_se< -4.694501 3 0 B (0.00000000 1.00000000) *
## 9) smoothness_worst< -1.594449 7 1 B (0.14285714 0.85714286)
## 18) smoothness_mean< -2.558761 1 0 M (1.00000000 0.00000000) *
## 19) smoothness_mean>=-2.558761 6 0 B (0.00000000 1.00000000) *
## 5) texture_worst>=4.642157 410 192 M (0.53170732 0.46829268)
## 10) texture_worst>=4.681966 369 155 M (0.57994580 0.42005420)
## 20) smoothness_worst< -1.452126 292 107 M (0.63356164 0.36643836)
## 40) smoothness_worst>=-1.50249 100 16 M (0.84000000 0.16000000)
## 80) smoothness_mean>=-2.339781 64 3 M (0.95312500 0.04687500) *
## 81) smoothness_mean< -2.339781 36 13 M (0.63888889 0.36111111) *
## 41) smoothness_worst< -1.50249 192 91 M (0.52604167 0.47395833)
## 82) symmetry_worst< -2.121358 28 3 M (0.89285714 0.10714286) *
## 83) symmetry_worst>=-2.121358 164 76 B (0.46341463 0.53658537) *
## 21) smoothness_worst>=-1.452126 77 29 B (0.37662338 0.62337662)
## 42) compactness_se>=-4.032549 33 12 M (0.63636364 0.36363636)
## 84) compactness_se< -3.425387 24 4 M (0.83333333 0.16666667) *
## 85) compactness_se>=-3.425387 9 1 B (0.11111111 0.88888889) *
## 43) compactness_se< -4.032549 44 8 B (0.18181818 0.81818182)
## 86) smoothness_worst>=-1.425207 5 1 M (0.80000000 0.20000000) *
## 87) smoothness_worst< -1.425207 39 4 B (0.10256410 0.89743590) *
## 11) texture_worst< 4.681966 41 4 B (0.09756098 0.90243902)
## 22) texture_mean>=3.067341 4 0 M (1.00000000 0.00000000) *
## 23) texture_mean< 3.067341 37 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.580648 404 178 B (0.44059406 0.55940594)
## 6) texture_worst< 4.543638 341 169 M (0.50439883 0.49560117)
## 12) smoothness_mean< -2.411844 86 23 M (0.73255814 0.26744186)
## 24) compactness_se< -3.483667 75 12 M (0.84000000 0.16000000)
## 48) symmetry_worst>=-2.079923 71 8 M (0.88732394 0.11267606)
## 96) texture_mean>=2.745392 68 5 M (0.92647059 0.07352941) *
## 97) texture_mean< 2.745392 3 0 B (0.00000000 1.00000000) *
## 49) symmetry_worst< -2.079923 4 0 B (0.00000000 1.00000000) *
## 25) compactness_se>=-3.483667 11 0 B (0.00000000 1.00000000) *
## 13) smoothness_mean>=-2.411844 255 109 B (0.42745098 0.57254902)
## 26) compactness_se>=-3.891799 149 62 M (0.58389262 0.41610738)
## 52) smoothness_worst>=-1.503711 93 27 M (0.70967742 0.29032258)
## 104) smoothness_mean< -2.271585 29 0 M (1.00000000 0.00000000) *
## 105) smoothness_mean>=-2.271585 64 27 M (0.57812500 0.42187500) *
## 53) smoothness_worst< -1.503711 56 21 B (0.37500000 0.62500000)
## 106) smoothness_worst< -1.565495 19 4 M (0.78947368 0.21052632) *
## 107) smoothness_worst>=-1.565495 37 6 B (0.16216216 0.83783784) *
## 27) compactness_se< -3.891799 106 22 B (0.20754717 0.79245283)
## 54) texture_worst>=4.531936 9 0 M (1.00000000 0.00000000) *
## 55) texture_worst< 4.531936 97 13 B (0.13402062 0.86597938)
## 110) symmetry_worst< -2.49184 4 0 M (1.00000000 0.00000000) *
## 111) symmetry_worst>=-2.49184 93 9 B (0.09677419 0.90322581) *
## 7) texture_worst>=4.543638 63 6 B (0.09523810 0.90476190)
## 14) texture_mean>=3.07959 3 0 M (1.00000000 0.00000000) *
## 15) texture_mean< 3.07959 60 3 B (0.05000000 0.95000000)
## 30) smoothness_mean< -2.486703 3 1 M (0.66666667 0.33333333)
## 60) texture_mean>=2.935975 2 0 M (1.00000000 0.00000000) *
## 61) texture_mean< 2.935975 1 0 B (0.00000000 1.00000000) *
## 31) smoothness_mean>=-2.486703 57 1 B (0.01754386 0.98245614)
## 62) compactness_se>=-3.096414 1 0 M (1.00000000 0.00000000) *
## 63) compactness_se< -3.096414 56 0 B (0.00000000 1.00000000) *
##
## $trees[[100]]
## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 412 M (0.54824561 0.45175439)
## 2) texture_worst>=4.824912 274 84 M (0.69343066 0.30656934)
## 4) smoothness_worst>=-1.623453 260 71 M (0.72692308 0.27307692)
## 8) symmetry_worst>=-2.207988 236 53 M (0.77542373 0.22457627)
## 16) symmetry_worst< -1.733593 115 10 M (0.91304348 0.08695652)
## 32) texture_worst>=4.897936 109 5 M (0.95412844 0.04587156)
## 64) compactness_se>=-4.706178 106 2 M (0.98113208 0.01886792) *
## 65) compactness_se< -4.706178 3 0 B (0.00000000 1.00000000) *
## 33) texture_worst< 4.897936 6 1 B (0.16666667 0.83333333)
## 66) texture_mean< 3.156152 1 0 M (1.00000000 0.00000000) *
## 67) texture_mean>=3.156152 5 0 B (0.00000000 1.00000000) *
## 17) symmetry_worst>=-1.733593 121 43 M (0.64462810 0.35537190)
## 34) symmetry_worst>=-1.703871 98 25 M (0.74489796 0.25510204)
## 68) compactness_se>=-4.418276 73 10 M (0.86301370 0.13698630) *
## 69) compactness_se< -4.418276 25 10 B (0.40000000 0.60000000) *
## 35) symmetry_worst< -1.703871 23 5 B (0.21739130 0.78260870)
## 70) texture_mean< 2.982883 5 0 M (1.00000000 0.00000000) *
## 71) texture_mean>=2.982883 18 0 B (0.00000000 1.00000000) *
## 9) symmetry_worst< -2.207988 24 6 B (0.25000000 0.75000000)
## 18) compactness_se>=-3.413706 6 0 M (1.00000000 0.00000000) *
## 19) compactness_se< -3.413706 18 0 B (0.00000000 1.00000000) *
## 5) smoothness_worst< -1.623453 14 1 B (0.07142857 0.92857143)
## 10) smoothness_mean>=-2.396135 1 0 M (1.00000000 0.00000000) *
## 11) smoothness_mean< -2.396135 13 0 B (0.00000000 1.00000000) *
## 3) texture_worst< 4.824912 638 310 B (0.48589342 0.51410658)
## 6) texture_worst< 4.747573 576 276 M (0.52083333 0.47916667)
## 12) texture_mean>=3.055881 72 8 M (0.88888889 0.11111111)
## 24) smoothness_worst>=-1.606352 52 0 M (1.00000000 0.00000000) *
## 25) smoothness_worst< -1.606352 20 8 M (0.60000000 0.40000000)
## 50) smoothness_worst< -1.678162 13 1 M (0.92307692 0.07692308)
## 100) texture_worst< 4.575818 12 0 M (1.00000000 0.00000000) *
## 101) texture_worst>=4.575818 1 0 B (0.00000000 1.00000000) *
## 51) smoothness_worst>=-1.678162 7 0 B (0.00000000 1.00000000) *
## 13) texture_mean< 3.055881 504 236 B (0.46825397 0.53174603)
## 26) compactness_se< -3.492332 348 155 M (0.55459770 0.44540230)
## 52) symmetry_worst< -1.503393 312 122 M (0.60897436 0.39102564)
## 104) smoothness_worst>=-1.451352 56 7 M (0.87500000 0.12500000) *
## 105) smoothness_worst< -1.451352 256 115 M (0.55078125 0.44921875) *
## 53) symmetry_worst>=-1.503393 36 3 B (0.08333333 0.91666667)
## 106) texture_mean>=2.998678 1 0 M (1.00000000 0.00000000) *
## 107) texture_mean< 2.998678 35 2 B (0.05714286 0.94285714) *
## 27) compactness_se>=-3.492332 156 43 B (0.27564103 0.72435897)
## 54) symmetry_worst>=-1.327359 20 6 M (0.70000000 0.30000000)
## 108) compactness_se< -2.646661 13 0 M (1.00000000 0.00000000) *
## 109) compactness_se>=-2.646661 7 1 B (0.14285714 0.85714286) *
## 55) symmetry_worst< -1.327359 136 29 B (0.21323529 0.78676471)
## 110) texture_worst< 4.530419 84 25 B (0.29761905 0.70238095) *
## 111) texture_worst>=4.530419 52 4 B (0.07692308 0.92307692) *
## 7) texture_worst>=4.747573 62 10 B (0.16129032 0.83870968)
## 14) symmetry_worst>=-1.758895 18 8 B (0.44444444 0.55555556)
## 28) smoothness_mean< -2.321477 10 2 M (0.80000000 0.20000000)
## 56) texture_mean< 3.091538 8 0 M (1.00000000 0.00000000) *
## 57) texture_mean>=3.091538 2 0 B (0.00000000 1.00000000) *
## 29) smoothness_mean>=-2.321477 8 0 B (0.00000000 1.00000000) *
## 15) symmetry_worst< -1.758895 44 2 B (0.04545455 0.95454545)
## 30) compactness_se>=-2.72933 2 0 M (1.00000000 0.00000000) *
## 31) compactness_se< -2.72933 42 0 B (0.00000000 1.00000000) *
##
##
## $weights
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##
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## [447,] 42.330540 15.239929
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## [449,] 40.125715 17.444754
## [450,] 39.913106 17.657363
## [451,] 47.532105 10.038364
## [452,] 16.118850 41.451619
## [453,] 42.329044 15.241425
## [454,] 39.490181 18.080288
## [455,] 45.031439 12.539030
## [456,] 42.252155 15.318315
## [457,] 40.758192 16.812278
## [458,] 41.595879 15.974590
## [459,] 41.057490 16.512979
## [460,] 49.393533 8.176937
## [461,] 39.165566 18.404903
## [462,] 40.339203 17.231267
## [463,] 47.917722 9.652747
## [464,] 50.001039 7.569430
## [465,] 40.380235 17.190235
## [466,] 47.007937 10.562532
## [467,] 41.793224 15.777245
## [468,] 8.933814 48.636655
## [469,] 40.432408 17.138061
## [470,] 43.805650 13.764819
## [471,] 47.539462 10.031007
## [472,] 42.225845 15.344624
## [473,] 46.100072 11.470397
## [474,] 40.375669 17.194801
## [475,] 46.191932 11.378537
## [476,] 40.029218 17.541252
## [477,] 46.434835 11.135634
## [478,] 44.552073 13.018396
## [479,] 42.426265 15.144204
## [480,] 41.714131 15.856338
## [481,] 41.776506 15.793963
## [482,] 16.493060 41.077409
## [483,] 40.690544 16.879925
## [484,] 41.080104 16.490365
## [485,] 39.939305 17.631164
## [486,] 42.745898 14.824571
## [487,] 44.878696 12.691773
## [488,] 44.053902 13.516567
## [489,] 41.431706 16.138764
## [490,] 15.344474 42.225995
## [491,] 15.177957 42.392512
## [492,] 15.476259 42.094210
## [493,] 10.545578 47.024891
## [494,] 14.244404 43.326065
## [495,] 38.799319 18.771150
## [496,] 44.315197 13.255272
## [497,] 42.169877 15.400592
## [498,] 46.276418 11.294052
## [499,] 15.288239 42.282230
## [500,] 7.554295 50.016174
## [501,] 17.402916 40.167553
## [502,] 10.690868 46.879601
## [503,] 48.224720 9.345749
## [504,] 46.689719 10.880750
## [505,] 15.710229 41.860240
## [506,] 14.430000 43.140470
## [507,] 18.491935 39.078534
## [508,] 11.854703 45.715766
## [509,] 39.312619 18.257850
## [510,] 15.527199 42.043270
## [511,] 44.506806 13.063663
## [512,] 40.078074 17.492395
## [513,] 15.594958 41.975511
## [514,] 41.099115 16.471354
## [515,] 40.706496 16.863973
## [516,] 43.487699 14.082770
## [517,] 15.889126 41.681343
## [518,] 18.642510 38.927959
## [519,] 44.848782 12.721687
## [520,] 43.443864 14.126605
## [521,] 16.531876 41.038593
## [522,] 40.071184 17.499285
## [523,] 40.415264 17.155205
## [524,] 18.633537 38.936933
## [525,] 16.026344 41.544125
## [526,] 15.487392 42.083078
## [527,] 42.539526 15.030944
## [528,] 17.333677 40.236792
## [529,] 41.764358 15.806111
## [530,] 15.713683 41.856786
## [531,] 17.135149 40.435321
## [532,] 15.523180 42.047289
## [533,] 40.750992 16.819477
## [534,] 41.158934 16.411536
## [535,] 7.723798 49.846671
## [536,] 16.389681 41.180788
## [537,] 17.834459 39.736010
## [538,] 17.844459 39.726010
## [539,] 41.467870 16.102599
## [540,] 17.882210 39.688259
## [541,] 41.881782 15.688687
## [542,] 17.835032 39.735437
## [543,] 15.581389 41.989081
## [544,] 16.930429 40.640040
## [545,] 15.647728 41.922741
## [546,] 15.413664 42.156805
## [547,] 15.929812 41.640658
## [548,] 46.226228 11.344241
## [549,] 16.237257 41.333213
## [550,] 39.627016 17.943453
## [551,] 42.366252 15.204217
## [552,] 9.645731 47.924738
## [553,] 13.445085 44.125384
## [554,] 40.970599 16.599870
## [555,] 40.768091 16.802378
## [556,] 15.091583 42.478886
## [557,] 40.969432 16.601037
## [558,] 16.267338 41.303131
## [559,] 42.326898 15.243571
## [560,] 16.546250 41.024219
## [561,] 43.045471 14.524998
## [562,] 14.259043 43.311426
## [563,] 40.292532 17.277937
## [564,] 7.481203 50.089266
## [565,] 39.615987 17.954482
## [566,] 15.931767 41.638703
## [567,] 13.471828 44.098642
## [568,] 8.316276 49.254193
## [569,] 42.924843 14.645626
## [570,] 16.065740 41.504730
## [571,] 17.156713 40.413756
## [572,] 10.631772 46.938697
## [573,] 15.030440 42.540029
## [574,] 17.436322 40.134148
## [575,] 13.445806 44.124663
## [576,] 10.846829 46.723640
## [577,] 16.188481 41.381989
## [578,] 15.056254 42.514216
## [579,] 15.778244 41.792225
## [580,] 11.691783 45.878686
## [581,] 11.304755 46.265714
## [582,] 17.513021 40.057449
## [583,] 39.496083 18.074386
## [584,] 41.178354 16.392116
## [585,] 17.777314 39.793155
## [586,] 15.696046 41.874424
## [587,] 12.923496 44.646973
## [588,] 39.619420 17.951049
## [589,] 41.692133 15.878336
## [590,] 15.694279 41.876190
## [591,] 11.553010 46.017459
## [592,] 40.291941 17.278529
## [593,] 40.846776 16.723693
## [594,] 16.241672 41.328797
## [595,] 11.874961 45.695508
## [596,] 10.162298 47.408171
## [597,] 12.883071 44.687398
## [598,] 13.170323 44.400146
## [599,] 9.398348 48.172121
## [600,] 41.918958 15.651511
## [601,] 48.498962 9.071507
## [602,] 13.631057 43.939412
## [603,] 40.055309 17.515161
## [604,] 16.591537 40.978932
## [605,] 39.118530 18.451940
## [606,] 15.304620 42.265849
## [607,] 16.517481 41.052988
## [608,] 12.853754 44.716716
## [609,] 45.137977 12.432492
## [610,] 14.537032 43.033438
## [611,] 13.569387 44.001082
## [612,] 42.492009 15.078460
## [613,] 42.629392 14.941077
## [614,] 40.764815 16.805654
## [615,] 39.471607 18.098862
## [616,] 42.049867 15.520603
## [617,] 17.585651 39.984818
## [618,] 40.307098 17.263371
## [619,] 46.929063 10.641406
## [620,] 45.253245 12.317225
## [621,] 17.670964 39.899505
## [622,] 13.894557 43.675912
## [623,] 42.015716 15.554753
## [624,] 18.124199 39.446270
## [625,] 10.889849 46.680620
## [626,] 17.230468 40.340001
## [627,] 40.340613 17.229856
## [628,] 41.919234 15.651235
## [629,] 44.414150 13.156319
## [630,] 42.626195 14.944274
## [631,] 13.309058 44.261412
## [632,] 40.906037 16.664432
## [633,] 40.787105 16.783365
## [634,] 10.658379 46.912090
## [635,] 17.446338 40.124132
## [636,] 45.553639 12.016831
## [637,] 15.962254 41.608215
## [638,] 9.320753 48.249716
## [639,] 16.248105 41.322364
## [640,] 16.581498 40.988971
## [641,] 43.439078 14.131392
## [642,] 41.460829 16.109640
## [643,] 15.100720 42.469749
## [644,] 15.171150 42.399319
## [645,] 39.542239 18.028230
## [646,] 16.644397 40.926072
## [647,] 16.568362 41.002107
## [648,] 41.409375 16.161094
## [649,] 41.437918 16.132551
## [650,] 14.571966 42.998503
## [651,] 16.519360 41.051109
## [652,] 10.381244 47.189226
## [653,] 16.939696 40.630774
## [654,] 42.417725 15.152745
## [655,] 16.485389 41.085080
## [656,] 13.099085 44.471384
## [657,] 15.572239 41.998230
## [658,] 17.488987 40.081482
## [659,] 41.796956 15.773513
## [660,] 40.442670 17.127799
## [661,] 41.359863 16.210606
## [662,] 41.504612 16.065858
## [663,] 41.419845 16.150624
## [664,] 45.865309 11.705160
## [665,] 46.580196 10.990273
## [666,] 39.690359 17.880110
## [667,] 39.373719 18.196751
## [668,] 40.435716 17.134753
## [669,] 39.642826 17.927643
## [670,] 40.089681 17.480788
## [671,] 39.871114 17.699355
## [672,] 15.376680 42.193789
## [673,] 12.812442 44.758027
## [674,] 15.239602 42.330868
## [675,] 17.225327 40.345142
## [676,] 10.133132 47.437337
## [677,] 16.477544 41.092925
## [678,] 41.901471 15.668999
## [679,] 15.534475 42.035994
## [680,] 40.119533 17.450936
## [681,] 11.954585 45.615884
## [682,] 14.639397 42.931072
## [683,] 39.630706 17.939763
## [684,] 10.985589 46.584880
## [685,] 12.121526 45.448943
## [686,] 14.045496 43.524973
## [687,] 41.545636 16.024834
## [688,] 39.745570 17.824899
## [689,] 10.946441 46.624028
## [690,] 15.525196 42.045273
## [691,] 7.163062 50.407408
## [692,] 15.934043 41.636426
## [693,] 16.382518 41.187951
## [694,] 13.627924 43.942545
## [695,] 17.151821 40.418649
## [696,] 16.684336 40.886133
## [697,] 14.491262 43.079207
## [698,] 10.642912 46.927557
## [699,] 8.653451 48.917019
## [700,] 13.839410 43.731059
## [701,] 16.046937 41.523532
## [702,] 15.241563 42.328906
## [703,] 41.365894 16.204575
## [704,] 15.072511 42.497959
## [705,] 15.683771 41.886698
## [706,] 15.443691 42.126778
## [707,] 10.751721 46.818748
## [708,] 7.775196 49.795273
## [709,] 7.157262 50.413207
## [710,] 16.393828 41.176642
## [711,] 6.821368 50.749101
## [712,] 14.473565 43.096904
## [713,] 10.112265 47.458204
## [714,] 11.940368 45.630102
## [715,] 40.946731 16.623738
## [716,] 16.017386 41.553083
## [717,] 12.399457 45.171012
## [718,] 14.558355 43.012114
## [719,] 40.030876 17.539593
## [720,] 13.413594 44.156876
## [721,] 48.632979 8.937490
## [722,] 15.171194 42.399275
## [723,] 10.071307 47.499162
## [724,] 40.970157 16.600312
## [725,] 42.510813 15.059656
## [726,] 39.658379 17.912090
## [727,] 17.287970 40.282499
## [728,] 16.317325 41.253144
## [729,] 10.559911 47.010558
## [730,] 12.582730 44.987739
## [731,] 41.943917 15.626552
## [732,] 16.409448 41.161021
## [733,] 41.113161 16.457308
## [734,] 16.095134 41.475335
## [735,] 15.016418 42.554051
## [736,] 16.302920 41.267549
## [737,] 9.252615 48.317854
## [738,] 13.991506 43.578964
## [739,] 16.388889 41.181580
## [740,] 13.425525 44.144944
## [741,] 11.970377 45.600093
## [742,] 44.484780 13.085690
## [743,] 13.092086 44.478384
## [744,] 17.216979 40.353490
## [745,] 6.958250 50.612219
## [746,] 16.103215 41.467254
## [747,] 10.366227 47.204242
## [748,] 15.331872 42.238597
## [749,] 17.619056 39.951413
## [750,] 17.083798 40.486671
## [751,] 40.863937 16.706532
## [752,] 17.718919 39.851550
## [753,] 40.847328 16.723141
## [754,] 40.767173 16.803297
## [755,] 46.609192 10.961278
## [756,] 40.092075 17.478394
## [757,] 40.468933 17.101537
## [758,] 15.927028 41.643441
## [759,] 13.194750 44.375719
## [760,] 15.644265 41.926204
## [761,] 14.827984 42.742485
## [762,] 13.780428 43.790041
## [763,] 48.692716 8.877753
## [764,] 12.853359 44.717110
## [765,] 17.070884 40.499585
## [766,] 14.479401 43.091068
## [767,] 40.006217 17.564252
## [768,] 10.294627 47.275842
## [769,] 13.735708 43.834761
## [770,] 41.678969 15.891500
## [771,] 10.508065 47.062404
## [772,] 16.516279 41.054191
## [773,] 42.340078 15.230392
## [774,] 46.045008 11.525462
## [775,] 17.768793 39.801676
## [776,] 12.280889 45.289580
## [777,] 14.388007 43.182462
## [778,] 11.805312 45.765157
## [779,] 18.261665 39.308804
## [780,] 41.854440 15.716029
## [781,] 14.563284 43.007185
## [782,] 13.342936 44.227534
## [783,] 16.874479 40.695990
## [784,] 15.461922 42.108547
## [785,] 43.126498 14.443971
## [786,] 15.720124 41.850345
## [787,] 16.125408 41.445061
## [788,] 13.679993 43.890476
## [789,] 16.623213 40.947256
## [790,] 39.828878 17.741592
## [791,] 17.358376 40.212093
## [792,] 13.405963 44.164506
## [793,] 15.687104 41.883365
## [794,] 16.637079 40.933390
## [795,] 15.389878 42.180591
## [796,] 16.502112 41.068357
## [797,] 16.305862 41.264607
## [798,] 13.452438 44.118032
## [799,] 15.366334 42.204135
## [800,] 17.151604 40.418865
## [801,] 6.399325 51.171144
## [802,] 11.830629 45.739841
## [803,] 40.884001 16.686468
## [804,] 16.932959 40.637510
## [805,] 45.189026 12.381443
## [806,] 45.402719 12.167750
## [807,] 16.024377 41.546092
## [808,] 41.435068 16.135401
## [809,] 16.948472 40.621998
## [810,] 15.730409 41.840060
## [811,] 14.845214 42.725255
## [812,] 11.166136 46.404333
## [813,] 41.544879 16.025590
## [814,] 12.529233 45.041236
## [815,] 17.258650 40.311819
## [816,] 38.772826 18.797643
## [817,] 39.802093 17.768376
## [818,] 13.463067 44.107402
## [819,] 11.860380 45.710089
## [820,] 39.423343 18.147127
## [821,] 17.141117 40.429352
## [822,] 40.789358 16.781111
## [823,] 17.613195 39.957275
## [824,] 12.639034 44.931435
## [825,] 18.047056 39.523413
## [826,] 15.492260 42.078210
## [827,] 15.925533 41.644936
## [828,] 12.695213 44.875256
## [829,] 42.214018 15.356451
## [830,] 11.499153 46.071316
## [831,] 16.096041 41.474429
## [832,] 15.943344 41.627125
## [833,] 18.420080 39.150390
## [834,] 16.695493 40.874976
## [835,] 41.226719 16.343751
## [836,] 16.714149 40.856320
## [837,] 15.985564 41.584905
## [838,] 14.683016 42.887453
## [839,] 12.589432 44.981037
## [840,] 16.952283 40.618186
## [841,] 14.133844 43.436626
## [842,] 40.301270 17.269199
## [843,] 15.368830 42.201639
## [844,] 15.785327 41.785142
## [845,] 16.975047 40.595422
## [846,] 15.752889 41.817580
## [847,] 10.548220 47.022249
## [848,] 14.368208 43.202261
## [849,] 46.342119 11.228350
## [850,] 11.527286 46.043183
## [851,] 42.000720 15.569750
## [852,] 40.382347 17.188122
## [853,] 7.428649 50.141820
## [854,] 13.691033 43.879436
## [855,] 15.718955 41.851514
## [856,] 16.378958 41.191511
## [857,] 41.758494 15.811975
## [858,] 16.117102 41.453367
## [859,] 39.822562 17.747907
## [860,] 16.322066 41.248403
## [861,] 15.027980 42.542490
## [862,] 18.514879 39.055590
## [863,] 16.306859 41.263610
## [864,] 43.721618 13.848851
## [865,] 8.997124 48.573345
## [866,] 12.453205 45.117264
## [867,] 45.263275 12.307195
## [868,] 14.433005 43.137464
## [869,] 40.072182 17.498288
## [870,] 15.927920 41.642549
## [871,] 41.736655 15.833815
## [872,] 41.480181 16.090288
## [873,] 15.844739 41.725730
## [874,] 16.785236 40.785233
## [875,] 40.065990 17.504479
## [876,] 14.849806 42.720663
## [877,] 17.065346 40.505123
## [878,] 10.247147 47.323322
## [879,] 17.685187 39.885282
## [880,] 8.112941 49.457529
## [881,] 16.438828 41.131641
## [882,] 18.344187 39.226282
## [883,] 14.295648 43.274821
## [884,] 40.367789 17.202680
## [885,] 15.223388 42.347081
## [886,] 16.907704 40.662766
## [887,] 15.586970 41.983499
## [888,] 14.215714 43.354755
## [889,] 18.114428 39.456042
## [890,] 16.348660 41.221809
## [891,] 15.930001 41.640468
## [892,] 18.425627 39.144842
## [893,] 13.531725 44.038744
## [894,] 17.526021 40.044449
## [895,] 15.619177 41.951293
## [896,] 15.159378 42.411091
## [897,] 16.940786 40.629683
## [898,] 16.673553 40.896916
## [899,] 16.390306 41.180164
## [900,] 15.724532 41.845937
## [901,] 14.799221 42.771248
## [902,] 18.300022 39.270447
## [903,] 14.353778 43.216691
## [904,] 13.852387 43.718082
## [905,] 16.834420 40.736049
## [906,] 17.289087 40.281382
## [907,] 13.422934 44.147536
## [908,] 49.214369 8.356100
## [909,] 42.330540 15.239929
## [910,] 41.158148 16.412322
## [911,] 40.125715 17.444754
## [912,] 47.532105 10.038364
##
## $prob
## [,1] [,2]
## [1,] 0.7352562 0.2647438
## [2,] 0.6859451 0.3140549
## [3,] 0.7821968 0.2178032
## [4,] 0.7339206 0.2660794
## [5,] 0.7079705 0.2920295
## [6,] 0.7262973 0.2737027
## [7,] 0.7225211 0.2774789
## [8,] 0.7795087 0.2204913
## [9,] 0.8579665 0.1420335
## [10,] 0.6803065 0.3196935
## [11,] 0.7477151 0.2522849
## [12,] 0.6781437 0.3218563
## [13,] 0.8323316 0.1676684
## [14,] 0.7014053 0.2985947
## [15,] 0.8165286 0.1834714
## [16,] 0.2608177 0.7391823
## [17,] 0.2057298 0.7942702
## [18,] 0.1551805 0.8448195
## [19,] 0.7023116 0.2976884
## [20,] 0.8257612 0.1742388
## [21,] 0.7334636 0.2665364
## [22,] 0.8007590 0.1992410
## [23,] 0.7013260 0.2986740
## [24,] 0.8023546 0.1976454
## [25,] 0.6953082 0.3046918
## [26,] 0.8076267 0.1923733
## [27,] 0.7738702 0.2261298
## [28,] 0.7121538 0.2878462
## [29,] 0.7369449 0.2630551
## [30,] 0.7245751 0.2754249
## [31,] 0.2864847 0.7135153
## [32,] 0.7067954 0.2932046
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##
## $class
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## [289] "B" "B" "B" "B" "M" "M" "M" "M" "M" "B" "M" "M" "B" "B" "B" "B" "B" "M"
## [307] "B" "B" "B" "M" "B" "M" "B" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B"
## [325] "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B"
## [343] "B" "B" "B" "B" "B" "M" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M"
## [361] "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "M" "B"
## [379] "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "M" "M" "B"
## [397] "B" "B" "B" "M" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M" "B" "M" "B" "M"
## [415] "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B"
## [433] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M"
## [451] "M" "B" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B"
## [469] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "B" "M" "M" "M" "M"
## [487] "M" "M" "M" "B" "B" "B" "B" "B" "M" "M" "M" "M" "B" "B" "B" "B" "M" "M"
## [505] "B" "B" "B" "B" "M" "B" "M" "M" "B" "M" "M" "M" "B" "B" "M" "M" "B" "M"
## [523] "M" "B" "B" "B" "M" "B" "M" "B" "B" "B" "M" "M" "B" "B" "B" "B" "M" "B"
## [541] "M" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "B" "M" "M" "B" "M" "B"
## [559] "M" "B" "M" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B"
## [577] "B" "B" "B" "B" "B" "B" "M" "M" "B" "B" "B" "M" "M" "B" "B" "M" "M" "B"
## [595] "B" "B" "B" "B" "B" "M" "M" "B" "M" "B" "M" "B" "B" "B" "M" "B" "B" "M"
## [613] "M" "M" "M" "M" "B" "M" "M" "M" "B" "B" "M" "B" "B" "B" "M" "M" "M" "M"
## [631] "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "M" "M" "B" "B" "M" "B" "B" "M"
## [649] "M" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "M" "M" "M" "M" "M" "M" "M"
## [667] "M" "M" "M" "M" "M" "B" "B" "B" "B" "B" "B" "M" "B" "M" "B" "B" "M" "B"
## [685] "B" "B" "M" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [703] "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B"
## [721] "M" "B" "B" "M" "M" "M" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B"
## [739] "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "M" "M"
## [757] "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "M" "B" "B" "M" "B" "B" "M" "M"
## [775] "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B" "B" "B" "M" "B" "B"
## [793] "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "M" "B" "M" "M" "B" "M" "B" "B"
## [811] "B" "B" "M" "B" "B" "M" "M" "B" "B" "M" "B" "M" "B" "B" "B" "B" "B" "B"
## [829] "M" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B" "B" "B" "M" "B" "B" "B" "B"
## [847] "B" "B" "M" "B" "M" "M" "B" "B" "B" "B" "M" "B" "M" "B" "B" "B" "B" "M"
## [865] "B" "B" "M" "B" "M" "B" "M" "M" "B" "B" "M" "B" "B" "B" "B" "B" "B" "B"
## [883] "B" "M" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [901] "B" "B" "B" "B" "B" "B" "B" "M" "M" "M" "M" "M"
##
## $importance
## compactness_se smoothness_mean smoothness_worst symmetry_worst
## 18.47356 17.49196 17.67883 16.51511
## texture_mean texture_worst
## 16.75694 13.08360
##
## $terms
## .outcome ~ texture_mean + smoothness_mean + compactness_se +
## texture_worst + smoothness_worst + symmetry_worst
## attr(,"variables")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"factors")
## texture_mean smoothness_mean compactness_se texture_worst
## .outcome 0 0 0 0
## texture_mean 1 0 0 0
## smoothness_mean 0 1 0 0
## compactness_se 0 0 1 0
## texture_worst 0 0 0 1
## smoothness_worst 0 0 0 0
## symmetry_worst 0 0 0 0
## smoothness_worst symmetry_worst
## .outcome 0 0
## texture_mean 0 0
## smoothness_mean 0 0
## compactness_se 0 0
## texture_worst 0 0
## smoothness_worst 1 0
## symmetry_worst 0 1
## attr(,"term.labels")
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
## attr(,"order")
## [1] 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: 0x000000003ec71dd8>
## attr(,"predvars")
## list(.outcome, texture_mean, smoothness_mean, compactness_se,
## texture_worst, smoothness_worst, symmetry_worst)
## attr(,"dataClasses")
## .outcome texture_mean smoothness_mean compactness_se
## "factor" "numeric" "numeric" "numeric"
## texture_worst smoothness_worst symmetry_worst
## "numeric" "numeric" "numeric"
##
## $call
## (function (formula, data, boos = TRUE, mfinal = 100, coeflearn = "Breiman",
## control, ...)
## {
## if (!(as.character(coeflearn) %in% c("Freund", "Breiman",
## "Zhu"))) {
## stop("coeflearn must be 'Freund', 'Breiman' or 'Zhu' ")
## }
## formula <- as.formula(formula)
## vardep <- data[, as.character(formula[[2]])]
## n <- length(data[, 1])
## nclases <- nlevels(vardep)
## pesos <- rep(1/n, n)
## guardarpesos <- array(0, c(n, mfinal))
## w <- rep(1/n, n)
## data <- cbind(pesos, data)
## arboles <- list()
## pond <- rep(0, mfinal)
## pred <- data.frame(rep(0, n))
## arboles[[1]] <- rpart(formula, data = data[, -1], control = rpart.control(minsplit = 1,
## cp = -1, maxdepth = 30))
## nvar <- dim(varImp(arboles[[1]], surrogates = FALSE, competes = FALSE))[1]
## imp <- array(0, c(mfinal, nvar))
## for (m in 1:mfinal) {
## if (boos == TRUE) {
## k <- 1
## while (k == 1) {
## boostrap <- sample(1:n, replace = TRUE, prob = pesos)
## fit <- rpart(formula, data = data[boostrap, -1],
## control = control)
## k <- length(fit$frame$var)
## }
## flearn <- predict(fit, newdata = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## if (boos == FALSE) {
## w <<- pesos
## fit <- rpart(formula = formula, data = data[, -1],
## weights = w, control = control)
## flearn <- predict(fit, data = data[, -1], type = "class")
## ind <- as.numeric(vardep != flearn)
## err <- sum(pesos * ind)
## }
## c <- log((1 - err)/err)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## guardarpesos[, m] <- pesos
## pesos <- pesos * exp(c * ind)
## pesos <- pesos/sum(pesos)
## maxerror <- 0.5
## eac <- 0.001
## if (coeflearn == "Zhu") {
## maxerror <- 1 - 1/nclases
## }
## if (err >= maxerror) {
## pesos <- rep(1/n, n)
## maxerror <- maxerror - eac
## c <- log((1 - maxerror)/maxerror)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## if (err == 0) {
## pesos <- rep(1/n, n)
## c <- log((1 - eac)/eac)
## if (coeflearn == "Breiman") {
## c <- (1/2) * c
## }
## if (coeflearn == "Zhu") {
## c <- c + log(nclases - 1)
## }
## }
## arboles[[m]] <- fit
## pond[m] <- c
## if (m == 1) {
## pred <- flearn
## }
## else {
## pred <- data.frame(pred, flearn)
## }
## if (length(fit$frame$var) > 1) {
## k <- varImp(fit, surrogates = FALSE, competes = FALSE)
## imp[m, ] <- k[sort(row.names(k)), ]
## }
## else {
## imp[m, ] <- rep(0, nvar)
## }
## }
## classfinal <- array(0, c(n, nlevels(vardep)))
## for (i in 1:nlevels(vardep)) {
## classfinal[, i] <- matrix(as.numeric(pred == levels(vardep)[i]),
## nrow = n) %*% as.vector(pond)
## }
## predclass <- rep("O", n)
## predclass[] <- apply(classfinal, 1, FUN = select, vardep.summary = summary(vardep))
## imppond <- as.vector(as.vector(pond) %*% imp)
## imppond <- imppond/sum(imppond) * 100
## names(imppond) <- sort(row.names(k))
## votosporc <- classfinal/apply(classfinal, 1, sum)
## ans <- list(formula = formula, trees = arboles, weights = pond,
## votes = classfinal, prob = votosporc, class = predclass,
## importance = imppond)
## attr(ans, "vardep.summary") <- summary(vardep, maxsum = 700)
## mf <- model.frame(formula = formula, data = data[, -1])
## terms <- attr(mf, "terms")
## ans$terms <- terms
## ans$call <- match.call()
## class(ans) <- "boosting"
## ans
## })(formula = .outcome ~ ., data = list(texture_mean = c(2.33988087773774,
## 2.87751164216656, 3.05635689537043, 3.01455402779458, 2.66305283517147,
## 2.75366071235426, 2.99473177322041, 3.08282698040492, 3.17971910966701,
## 3.14587493198371, 2.88424189752063, 3.17596832385692, 3.11839228628988,
## 3.0022112396517, 3.02916704964023, 2.66444656362008, 2.75429745226753,
## 2.52091708731103, 2.65745841498615, 3.0624559055969, 2.79728133483015,
## 3.06944731137627, 3.00815479355255, 3.2296179214001, 2.71137799119488,
## 2.92852352386054, 3.17722014959937, 3.27601201623901, 2.88368276974537,
## 3.07223024452672, 2.91343703082716, 3.22684399451738, 3.03591406318682,
## 3.07176695982999, 3.21124679770371, 3.08236858021354, 2.82375700881418,
## 2.68307421503203, 3.10458667846607, 3.07269331469012, 2.79361608943186,
## 2.90361698464619, 3.09195113129453, 2.93119375241642, 2.92154737536461,
## 3.07223024452672, 2.96062309644042, 2.46725171454928, 3.04356960296815,
## 3.09783749649114, 2.62900699376176, 3.17555070012983, 3.04499851485691,
## 2.94654202936322, 3.19948911106801, 2.75937682826755, 2.80457176809283,
## 2.97807733831527, 2.39242579699384, 2.78192004966867, 3.17680304844629,
## 2.89037175789616, 3.04309284491383, 3.21526932927409, 3.26918863874179,
## 2.75047091698616, 2.91885122921803, 3.06619073720255, 3.2023398562281,
## 2.7239235502585, 3.17888681665184, 3.12500460925813, 2.90690105984738,
## 2.9871959425317, 3.13679771383259, 2.88144312715186, 2.55256529826182,
## 2.98416563718253, 3.21807550469743, 2.59749101053515, 2.95958682691764,
## 2.74470351875025, 2.91993056013771, 3.0568273729138, 2.83262493568384,
## 3.03302805829769, 2.97807733831527, 3.00518743232475, 2.76190687389292,
## 2.75747508442973, 2.8136106967627, 3.1315734964654, 2.99623214859564,
## 2.38139627341834, 2.8402473707136, 2.38784493694487, 2.84549061022345,
## 3.20639830335709, 2.93969088267037, 2.58701187272515, 2.96938829821439,
## 3.06991167172824, 2.63404478779171, 3.08694315360738, 2.8136106967627,
## 2.73371794785079, 2.59450815970308, 2.48240351956988, 2.89314568477889,
## 2.85128436918812, 2.76757618041624, 2.70604819843154, 2.68444033546308,
## 2.93225985059842, 3.03399098567108, 3.03013370027132, 2.73046379593911,
## 2.57108434602905, 2.73046379593911, 2.88703285663065, 3.03206420280138,
## 2.96836107675786, 2.54474665014402, 2.56186769092413, 3.00469201492546,
## 2.89867056071086, 3.10099278421148, 3.09285898428471, 2.98365969231972,
## 2.9338568698359, 3.20599319903719, 2.83026783382646, 2.51688969564105,
## 2.97705900828837, 2.47569771070269, 2.68852753461335, 2.71800053195538,
## 2.67069441455844, 2.89369954798884, 3.00121720378456, 3.10099278421148,
## 2.56955412384829, 3.08511583468868, 3.27978275977172, 3.01111337559229,
## 2.70270259477561, 2.71535677628465, 2.92208573338569, 2.84432781939476,
## 2.85589532836619, 2.76631910922619, 3.06385810260159, 2.90251989183181,
## 3.14458322028635, 2.79300390698237, 3.0837431508767, 3.11307076597122,
## 2.96114082878437, 3.28353933819392, 3.16758253048065, 2.92316158071916,
## 2.8142103969306, 2.84897089215859, 3.00864849882054, 2.55800220485855,
## 2.94127608775793, 3.24102862950933, 3.17010566049877, 2.82908719614504,
## 2.86105737022739, 3.0708397460408, 3.48031658611475, 3.00815479355255,
## 2.83438912314523, 2.60046499042227, 2.73825604315928, 3.17680304844629,
## 3.10593106585207, 2.94864066602014, 3.29879544804407, 3.52075661671979,
## 3.32539566824587, 2.7669478423497, 3.05635689537043, 3.06619073720255,
## 3.67071548348627, 2.74727091425549, 2.90087199253003, 3.1684242813721,
## 3.15700042115011, 2.85819285953193, 2.64688376586472, 3.22763733053677,
## 2.7033726115511, 3.15955035878339, 2.91506437048654, 2.98669152890184,
## 2.83790818836042, 2.96165829322024, 3.35933317756346, 2.84897089215859,
## 3.51333488159901, 3.29805662274264, 2.96424160646262, 3.09421922026864,
## 2.94180393152844, 3.0837431508767, 2.78562833574758, 3.01504458458636,
## 2.8225686545448, 3.04404613383254, 2.8541687092322, 2.65042108826557,
## 2.99473177322041, 2.88144312715186, 2.71997877196748, 3.28091121578765,
## 2.64048488160644, 2.90032208874933, 2.93225985059842, 2.91235066461494,
## 3.03302805829769, 2.57413778351594, 2.99373027088332, 2.93863268151342,
## 2.98214032003452, 2.94968833505258, 2.77383794164021, 2.85991255041146,
## 2.62321826558551, 2.58550584834412, 2.51365606307399, 2.89811944468699,
## 2.89977188240808, 2.9391619220656, 2.99021709286588, 3.17220341666977,
## 2.92369907065416, 2.89922137317315, 3.19826487096408, 2.76127496233951,
## 2.54238908520136, 2.62756295018952, 2.95021175825218, 2.75302356674494,
## 2.59301339111385, 2.37211115564266, 2.92316158071916, 2.82435065679837,
## 2.6447553507299, 2.93757335938046, 2.9391619220656, 2.83321334405622,
## 2.78377591163035, 2.97858611471902, 2.58926666511224, 3.06851794327964,
## 2.7219531062712, 2.85070650150373, 2.55567572067621, 3.03061667540749,
## 3.08557297755378, 2.74148497718845, 2.96269241947579, 2.98870765861703,
## 2.69327491552006, 2.94549105711724, 3.04452243772342, 2.65535241210176,
## 3.06479180948549, 2.86391369893314, 3.18924101973851, 2.80578168959555,
## 2.82375700881418, 2.70537997254633, 3.07639017657145, 2.68852753461335,
## 2.9391619220656, 2.69056488676119, 2.70537997254633, 2.83732253680635,
## 2.95595140354215, 2.85991255041146, 3.24804620216798, 2.6440448711263,
## 2.92262380173335, 2.78562833574758, 2.74019465442878, 2.90799335924598,
## 2.89425310460414, 3.07130346040107, 2.90635446240277, 3.08099211750481,
## 3.28952066443753, 2.84781214347737, 3.08648663682246, 3.14802408389625,
## 2.58097411853423, 2.71469474382088, 2.85359250639287, 2.77695417974942,
## 2.77695417974942, 3.00667221359233, 3.33967652501391, 2.71800053195538,
## 2.93545134266906, 2.56186769092413, 2.86105737022739, 2.61885462229774,
## 3.14802408389625, 2.7408400239252, 3.14458322028635, 2.50307395374345,
## 2.82375700881418, 2.99423114742772, 3.10368941505908, 2.87469394517693,
## 2.84374591655611, 2.93863268151342, 2.85991255041146, 2.69665215614984,
## 2.84839168565528, 2.38967979984498, 2.78315767358902, 2.7047112998367,
## 2.92262380173335, 2.69867303928961, 3.06198806933106, 3.02819946369149,
## 2.88591740754678, 2.86619290219901, 2.82316300820271, 3.07639017657145,
## 3.09602999486936, 3.39484390768998, 3.05258508514677, 3.04832472367316,
## 2.49897390699944, 2.94654202936322, 2.63762773680566, 2.77383794164021,
## 2.95125778345216, 2.95073490762326, 3.05776766447344, 2.70671597808907,
## 2.8106067894273, 2.87186828633161, 3.11484775444415, 2.8724340572095,
## 2.97246364661464, 2.82967768922391, 2.97654945413722, 2.97246364661464,
## 2.77133794033813, 2.97552956623647, 2.75110969056266, 2.84490938381941,
## 2.75937682826755, 3.2144661163795, 3.3332753651767, 2.87130219517581,
## 2.96217549002515, 3.02140002030257, 3.06991167172824, 3.2188758248682,
## 3.3403852422654, 3.42491390827947, 3.37724616083964, 3.22882615572137,
## 3.2240623515555, 3.30137704637994, 2.91017438519234, 2.90251989183181,
## 3.0022112396517, 3.03206420280138, 2.89591193827178, 3.14974008603334,
## 2.90032208874933, 2.91723004539903, 2.7033726115511, 3.40019688132857,
## 2.74855214441154, 2.75556971707019, 3.02188723103084, 2.8106067894273,
## 2.68033636253469, 2.97092715463502, 2.89203703721523, 2.95699144523756,
## 2.64333388638252, 2.42303124606991, 2.797890905102, 2.82435065679837,
## 2.93492013415723, 3.00568260440716, 3.1108450806545, 2.58248697812686,
## 3.02237420450041, 3.00617753141553, 2.86334308550825, 3.05588619637374,
## 2.79239134953596, 2.9871959425317, 2.55489902160804, 2.57566101305646,
## 2.8402473707136, 3.17596832385692, 2.68716699018579, 2.68784749378469,
## 3.02140002030257, 2.61447185414264, 2.94811641961233, 2.92369907065416,
## 3.0243197304059, 3.00864849882054, 2.90251989183181, 2.81540871942271,
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## 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
## 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
## 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
## 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
## 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
## 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
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## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L)),
## mfinal = 100, coeflearn = "Breiman", control = list(minsplit = 0,
## minbucket = 0, cp = -1, maxcompete = 4L, maxsurrogate = 5L,
## usesurrogate = 2L, surrogatestyle = 0L, maxdepth = 6,
## xval = 0))
##
## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## mfinal maxdepth coeflearn
## 6 100 6 Breiman
##
## $obsLevels
## [1] "M" "B"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"vardep.summary")
## M B
## 340 572
## attr(,"class")
## [1] "boosting"
MBS_AB_Tune$results## coeflearn maxdepth mfinal ROC Sens Spec ROCSD SensSD
## 1 Breiman 4 50 0.9506186 0.8800000 0.9335561 0.01896836 0.04405224
## 3 Breiman 5 50 0.9575898 0.8941176 0.9412265 0.01767991 0.05369829
## 5 Breiman 6 50 0.9619147 0.8988235 0.9415927 0.01476659 0.04458899
## 2 Breiman 4 100 0.9575073 0.8994118 0.9384409 0.01794364 0.04908329
## 4 Breiman 5 100 0.9631176 0.8982353 0.9412265 0.01755778 0.04931041
## 6 Breiman 6 100 0.9647554 0.9011765 0.9398352 0.01396669 0.04868513
## SpecSD
## 1 0.02758453
## 3 0.02202985
## 5 0.02460794
## 2 0.02898207
## 4 0.02160683
## 6 0.02269900
(MBS_AB_Train_AUROC <- MBS_AB_Tune$results[MBS_AB_Tune$results$mfinal==MBS_AB_Tune$bestTune$mfinal &
MBS_AB_Tune$results$maxdepth==MBS_AB_Tune$bestTune$maxdepth &
MBS_AB_Tune$results$coeflearn==MBS_AB_Tune$bestTune$coeflearn,
c("ROC")])## [1] 0.9647554
##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_AB_VarImp <- varImp(MBS_AB_Tune, scale = TRUE)
plot(MBS_AB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Adaptive Boosting",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
MBS_AB_Test <- data.frame(MBS_AB_Test_Observed = MA_Test$diagnosis,
MBS_AB_Test_Predicted = predict(MBS_AB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_AB_Test_ROC <- roc(response = MBS_AB_Test$MBS_AB_Test_Observed,
predictor = MBS_AB_Test$MBS_AB_Test_Predicted.M,
levels = rev(levels(MBS_AB_Test$MBS_AB_Test_Observed)))
(MBS_AB_Test_AUROC <- auc(MBS_AB_Test_ROC)[1])## [1] 0.9936284
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
GBM_Grid = expand.grid(n.trees = 500,
interaction.depth = c(4,5,6),
shrinkage = c(0.1,0.01,0.001),
n.minobsinnode = c(5, 10, 15))
##################################
# Running the stochastic gradient boosting model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
MBS_GBM_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "gbm",
tuneGrid = GBM_Grid,
metric = "ROC",
trControl = RKFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0003
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0005
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0003
## 120 1.2202 nan 0.0010 0.0004
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1908 nan 0.0010 0.0003
## 180 1.1771 nan 0.0010 0.0003
## 200 1.1639 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1384 nan 0.0010 0.0003
## 260 1.1261 nan 0.0010 0.0003
## 280 1.1143 nan 0.0010 0.0003
## 300 1.1027 nan 0.0010 0.0002
## 320 1.0915 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0003
## 360 1.0701 nan 0.0010 0.0002
## 380 1.0599 nan 0.0010 0.0002
## 400 1.0500 nan 0.0010 0.0002
## 420 1.0404 nan 0.0010 0.0002
## 440 1.0309 nan 0.0010 0.0002
## 460 1.0219 nan 0.0010 0.0002
## 480 1.0129 nan 0.0010 0.0002
## 500 1.0042 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0005
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0005
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0004
## 100 1.2371 nan 0.0010 0.0004
## 120 1.2217 nan 0.0010 0.0004
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1925 nan 0.0010 0.0003
## 180 1.1784 nan 0.0010 0.0003
## 200 1.1651 nan 0.0010 0.0003
## 220 1.1524 nan 0.0010 0.0003
## 240 1.1396 nan 0.0010 0.0003
## 260 1.1271 nan 0.0010 0.0003
## 280 1.1153 nan 0.0010 0.0003
## 300 1.1037 nan 0.0010 0.0003
## 320 1.0924 nan 0.0010 0.0002
## 340 1.0816 nan 0.0010 0.0002
## 360 1.0711 nan 0.0010 0.0002
## 380 1.0608 nan 0.0010 0.0002
## 400 1.0509 nan 0.0010 0.0002
## 420 1.0410 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0224 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
## 500 1.0047 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0003
## 140 1.2079 nan 0.0010 0.0003
## 160 1.1940 nan 0.0010 0.0003
## 180 1.1803 nan 0.0010 0.0003
## 200 1.1671 nan 0.0010 0.0003
## 220 1.1540 nan 0.0010 0.0003
## 240 1.1416 nan 0.0010 0.0003
## 260 1.1298 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0003
## 300 1.1065 nan 0.0010 0.0002
## 320 1.0956 nan 0.0010 0.0003
## 340 1.0848 nan 0.0010 0.0002
## 360 1.0744 nan 0.0010 0.0002
## 380 1.0642 nan 0.0010 0.0002
## 400 1.0542 nan 0.0010 0.0002
## 420 1.0446 nan 0.0010 0.0002
## 440 1.0353 nan 0.0010 0.0002
## 460 1.0260 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0084 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2479 nan 0.0010 0.0003
## 100 1.2310 nan 0.0010 0.0003
## 120 1.2152 nan 0.0010 0.0003
## 140 1.1995 nan 0.0010 0.0003
## 160 1.1842 nan 0.0010 0.0003
## 180 1.1696 nan 0.0010 0.0003
## 200 1.1553 nan 0.0010 0.0003
## 220 1.1416 nan 0.0010 0.0003
## 240 1.1285 nan 0.0010 0.0002
## 260 1.1155 nan 0.0010 0.0003
## 280 1.1031 nan 0.0010 0.0003
## 300 1.0909 nan 0.0010 0.0002
## 320 1.0792 nan 0.0010 0.0003
## 340 1.0676 nan 0.0010 0.0003
## 360 1.0562 nan 0.0010 0.0002
## 380 1.0456 nan 0.0010 0.0002
## 400 1.0351 nan 0.0010 0.0002
## 420 1.0249 nan 0.0010 0.0002
## 440 1.0149 nan 0.0010 0.0002
## 460 1.0054 nan 0.0010 0.0002
## 480 0.9959 nan 0.0010 0.0002
## 500 0.9867 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0004
## 120 1.2168 nan 0.0010 0.0003
## 140 1.2010 nan 0.0010 0.0003
## 160 1.1860 nan 0.0010 0.0003
## 180 1.1716 nan 0.0010 0.0003
## 200 1.1574 nan 0.0010 0.0003
## 220 1.1436 nan 0.0010 0.0003
## 240 1.1304 nan 0.0010 0.0003
## 260 1.1174 nan 0.0010 0.0003
## 280 1.1051 nan 0.0010 0.0002
## 300 1.0930 nan 0.0010 0.0003
## 320 1.0812 nan 0.0010 0.0003
## 340 1.0698 nan 0.0010 0.0002
## 360 1.0589 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0003
## 400 1.0374 nan 0.0010 0.0002
## 420 1.0270 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0073 nan 0.0010 0.0002
## 480 0.9980 nan 0.0010 0.0002
## 500 0.9890 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2492 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0003
## 120 1.2172 nan 0.0010 0.0003
## 140 1.2019 nan 0.0010 0.0004
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1727 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1454 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1195 nan 0.0010 0.0002
## 280 1.1069 nan 0.0010 0.0002
## 300 1.0950 nan 0.0010 0.0003
## 320 1.0835 nan 0.0010 0.0002
## 340 1.0719 nan 0.0010 0.0003
## 360 1.0610 nan 0.0010 0.0002
## 380 1.0503 nan 0.0010 0.0002
## 400 1.0401 nan 0.0010 0.0002
## 420 1.0298 nan 0.0010 0.0002
## 440 1.0200 nan 0.0010 0.0002
## 460 1.0102 nan 0.0010 0.0002
## 480 1.0008 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0005
## 10 1.3112 nan 0.0010 0.0005
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2634 nan 0.0010 0.0004
## 80 1.2455 nan 0.0010 0.0004
## 100 1.2280 nan 0.0010 0.0004
## 120 1.2111 nan 0.0010 0.0004
## 140 1.1948 nan 0.0010 0.0004
## 160 1.1793 nan 0.0010 0.0003
## 180 1.1643 nan 0.0010 0.0003
## 200 1.1497 nan 0.0010 0.0003
## 220 1.1354 nan 0.0010 0.0003
## 240 1.1217 nan 0.0010 0.0002
## 260 1.1082 nan 0.0010 0.0003
## 280 1.0952 nan 0.0010 0.0003
## 300 1.0828 nan 0.0010 0.0003
## 320 1.0705 nan 0.0010 0.0003
## 340 1.0587 nan 0.0010 0.0003
## 360 1.0473 nan 0.0010 0.0002
## 380 1.0361 nan 0.0010 0.0003
## 400 1.0250 nan 0.0010 0.0002
## 420 1.0142 nan 0.0010 0.0002
## 440 1.0037 nan 0.0010 0.0002
## 460 0.9937 nan 0.0010 0.0002
## 480 0.9839 nan 0.0010 0.0002
## 500 0.9742 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0005
## 60 1.2637 nan 0.0010 0.0005
## 80 1.2459 nan 0.0010 0.0004
## 100 1.2288 nan 0.0010 0.0004
## 120 1.2120 nan 0.0010 0.0004
## 140 1.1959 nan 0.0010 0.0004
## 160 1.1805 nan 0.0010 0.0003
## 180 1.1653 nan 0.0010 0.0003
## 200 1.1507 nan 0.0010 0.0003
## 220 1.1366 nan 0.0010 0.0003
## 240 1.1230 nan 0.0010 0.0003
## 260 1.1099 nan 0.0010 0.0003
## 280 1.0969 nan 0.0010 0.0003
## 300 1.0842 nan 0.0010 0.0003
## 320 1.0720 nan 0.0010 0.0003
## 340 1.0602 nan 0.0010 0.0002
## 360 1.0487 nan 0.0010 0.0002
## 380 1.0375 nan 0.0010 0.0003
## 400 1.0267 nan 0.0010 0.0002
## 420 1.0163 nan 0.0010 0.0002
## 440 1.0060 nan 0.0010 0.0002
## 460 0.9959 nan 0.0010 0.0002
## 480 0.9863 nan 0.0010 0.0002
## 500 0.9766 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0005
## 60 1.2647 nan 0.0010 0.0004
## 80 1.2471 nan 0.0010 0.0004
## 100 1.2300 nan 0.0010 0.0003
## 120 1.2137 nan 0.0010 0.0004
## 140 1.1976 nan 0.0010 0.0004
## 160 1.1825 nan 0.0010 0.0003
## 180 1.1676 nan 0.0010 0.0003
## 200 1.1533 nan 0.0010 0.0003
## 220 1.1395 nan 0.0010 0.0003
## 240 1.1261 nan 0.0010 0.0003
## 260 1.1129 nan 0.0010 0.0003
## 280 1.1001 nan 0.0010 0.0003
## 300 1.0877 nan 0.0010 0.0003
## 320 1.0758 nan 0.0010 0.0002
## 340 1.0643 nan 0.0010 0.0002
## 360 1.0531 nan 0.0010 0.0002
## 380 1.0422 nan 0.0010 0.0002
## 400 1.0315 nan 0.0010 0.0002
## 420 1.0209 nan 0.0010 0.0002
## 440 1.0106 nan 0.0010 0.0002
## 460 1.0006 nan 0.0010 0.0002
## 480 0.9910 nan 0.0010 0.0002
## 500 0.9816 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0048
## 3 1.2939 nan 0.0100 0.0039
## 4 1.2852 nan 0.0100 0.0037
## 5 1.2763 nan 0.0100 0.0038
## 6 1.2681 nan 0.0100 0.0039
## 7 1.2599 nan 0.0100 0.0039
## 8 1.2523 nan 0.0100 0.0032
## 9 1.2440 nan 0.0100 0.0037
## 10 1.2357 nan 0.0100 0.0035
## 20 1.1634 nan 0.0100 0.0029
## 40 1.0487 nan 0.0100 0.0023
## 60 0.9632 nan 0.0100 0.0015
## 80 0.8958 nan 0.0100 0.0012
## 100 0.8410 nan 0.0100 0.0010
## 120 0.7963 nan 0.0100 0.0008
## 140 0.7597 nan 0.0100 0.0005
## 160 0.7265 nan 0.0100 0.0005
## 180 0.7008 nan 0.0100 0.0003
## 200 0.6782 nan 0.0100 0.0002
## 220 0.6575 nan 0.0100 0.0003
## 240 0.6391 nan 0.0100 -0.0001
## 260 0.6229 nan 0.0100 0.0001
## 280 0.6073 nan 0.0100 -0.0001
## 300 0.5937 nan 0.0100 0.0000
## 320 0.5824 nan 0.0100 0.0001
## 340 0.5711 nan 0.0100 0.0000
## 360 0.5597 nan 0.0100 0.0000
## 380 0.5500 nan 0.0100 -0.0001
## 400 0.5398 nan 0.0100 -0.0000
## 420 0.5296 nan 0.0100 -0.0000
## 440 0.5201 nan 0.0100 0.0001
## 460 0.5116 nan 0.0100 -0.0001
## 480 0.5030 nan 0.0100 -0.0002
## 500 0.4955 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0047
## 2 1.3022 nan 0.0100 0.0038
## 3 1.2943 nan 0.0100 0.0036
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2769 nan 0.0100 0.0043
## 6 1.2690 nan 0.0100 0.0036
## 7 1.2604 nan 0.0100 0.0039
## 8 1.2519 nan 0.0100 0.0039
## 9 1.2438 nan 0.0100 0.0038
## 10 1.2360 nan 0.0100 0.0037
## 20 1.1652 nan 0.0100 0.0032
## 40 1.0492 nan 0.0100 0.0017
## 60 0.9614 nan 0.0100 0.0017
## 80 0.8937 nan 0.0100 0.0011
## 100 0.8402 nan 0.0100 0.0007
## 120 0.7961 nan 0.0100 0.0007
## 140 0.7584 nan 0.0100 0.0004
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## 180 0.7027 nan 0.0100 0.0001
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## 240 0.6425 nan 0.0100 0.0001
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## 280 0.6116 nan 0.0100 0.0001
## 300 0.5979 nan 0.0100 -0.0000
## 320 0.5845 nan 0.0100 0.0002
## 340 0.5735 nan 0.0100 0.0000
## 360 0.5626 nan 0.0100 0.0000
## 380 0.5523 nan 0.0100 0.0000
## 400 0.5424 nan 0.0100 -0.0000
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## 460 0.5157 nan 0.0100 0.0001
## 480 0.5072 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3015 nan 0.0100 0.0041
## 3 1.2933 nan 0.0100 0.0035
## 4 1.2839 nan 0.0100 0.0042
## 5 1.2754 nan 0.0100 0.0041
## 6 1.2673 nan 0.0100 0.0033
## 7 1.2596 nan 0.0100 0.0037
## 8 1.2511 nan 0.0100 0.0038
## 9 1.2434 nan 0.0100 0.0033
## 10 1.2357 nan 0.0100 0.0034
## 20 1.1644 nan 0.0100 0.0029
## 40 1.0521 nan 0.0100 0.0016
## 60 0.9660 nan 0.0100 0.0015
## 80 0.8986 nan 0.0100 0.0015
## 100 0.8443 nan 0.0100 0.0009
## 120 0.7998 nan 0.0100 0.0006
## 140 0.7647 nan 0.0100 0.0006
## 160 0.7346 nan 0.0100 0.0003
## 180 0.7098 nan 0.0100 0.0001
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## 220 0.6672 nan 0.0100 0.0002
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## 320 0.5919 nan 0.0100 0.0000
## 340 0.5802 nan 0.0100 0.0002
## 360 0.5693 nan 0.0100 0.0001
## 380 0.5600 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2929 nan 0.0100 0.0044
## 4 1.2836 nan 0.0100 0.0042
## 5 1.2743 nan 0.0100 0.0039
## 6 1.2662 nan 0.0100 0.0035
## 7 1.2575 nan 0.0100 0.0041
## 8 1.2482 nan 0.0100 0.0043
## 9 1.2399 nan 0.0100 0.0043
## 10 1.2314 nan 0.0100 0.0039
## 20 1.1558 nan 0.0100 0.0033
## 40 1.0344 nan 0.0100 0.0020
## 60 0.9422 nan 0.0100 0.0017
## 80 0.8705 nan 0.0100 0.0013
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## 160 0.6942 nan 0.0100 0.0003
## 180 0.6666 nan 0.0100 0.0002
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## 280 0.5687 nan 0.0100 -0.0000
## 300 0.5544 nan 0.0100 0.0000
## 320 0.5414 nan 0.0100 0.0000
## 340 0.5279 nan 0.0100 -0.0000
## 360 0.5154 nan 0.0100 0.0002
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## 460 0.4628 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0048
## 2 1.3028 nan 0.0100 0.0044
## 3 1.2933 nan 0.0100 0.0046
## 4 1.2838 nan 0.0100 0.0043
## 5 1.2752 nan 0.0100 0.0036
## 6 1.2655 nan 0.0100 0.0044
## 7 1.2567 nan 0.0100 0.0040
## 8 1.2477 nan 0.0100 0.0042
## 9 1.2397 nan 0.0100 0.0035
## 10 1.2316 nan 0.0100 0.0041
## 20 1.1569 nan 0.0100 0.0033
## 40 1.0356 nan 0.0100 0.0023
## 60 0.9436 nan 0.0100 0.0017
## 80 0.8721 nan 0.0100 0.0013
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## 220 0.6256 nan 0.0100 0.0002
## 240 0.6068 nan 0.0100 -0.0000
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## 280 0.5730 nan 0.0100 0.0003
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## 320 0.5468 nan 0.0100 0.0000
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## 380 0.5101 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3016 nan 0.0100 0.0049
## 3 1.2923 nan 0.0100 0.0045
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2745 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0038
## 7 1.2579 nan 0.0100 0.0038
## 8 1.2494 nan 0.0100 0.0038
## 9 1.2413 nan 0.0100 0.0033
## 10 1.2331 nan 0.0100 0.0037
## 20 1.1592 nan 0.0100 0.0027
## 40 1.0416 nan 0.0100 0.0022
## 60 0.9520 nan 0.0100 0.0018
## 80 0.8812 nan 0.0100 0.0011
## 100 0.8246 nan 0.0100 0.0010
## 120 0.7767 nan 0.0100 0.0009
## 140 0.7376 nan 0.0100 0.0007
## 160 0.7065 nan 0.0100 0.0004
## 180 0.6781 nan 0.0100 0.0003
## 200 0.6528 nan 0.0100 0.0004
## 220 0.6337 nan 0.0100 0.0002
## 240 0.6153 nan 0.0100 0.0001
## 260 0.5994 nan 0.0100 0.0001
## 280 0.5829 nan 0.0100 0.0001
## 300 0.5690 nan 0.0100 0.0001
## 320 0.5556 nan 0.0100 -0.0001
## 340 0.5424 nan 0.0100 0.0000
## 360 0.5312 nan 0.0100 -0.0000
## 380 0.5196 nan 0.0100 -0.0001
## 400 0.5087 nan 0.0100 0.0000
## 420 0.4985 nan 0.0100 -0.0002
## 440 0.4892 nan 0.0100 -0.0001
## 460 0.4801 nan 0.0100 -0.0001
## 480 0.4705 nan 0.0100 0.0000
## 500 0.4617 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3011 nan 0.0100 0.0046
## 3 1.2915 nan 0.0100 0.0043
## 4 1.2820 nan 0.0100 0.0040
## 5 1.2720 nan 0.0100 0.0045
## 6 1.2632 nan 0.0100 0.0041
## 7 1.2545 nan 0.0100 0.0040
## 8 1.2452 nan 0.0100 0.0038
## 9 1.2366 nan 0.0100 0.0041
## 10 1.2279 nan 0.0100 0.0038
## 20 1.1490 nan 0.0100 0.0030
## 40 1.0226 nan 0.0100 0.0024
## 60 0.9282 nan 0.0100 0.0016
## 80 0.8528 nan 0.0100 0.0013
## 100 0.7941 nan 0.0100 0.0007
## 120 0.7452 nan 0.0100 0.0008
## 140 0.7043 nan 0.0100 0.0006
## 160 0.6694 nan 0.0100 0.0006
## 180 0.6405 nan 0.0100 0.0002
## 200 0.6155 nan 0.0100 0.0001
## 220 0.5917 nan 0.0100 0.0003
## 240 0.5719 nan 0.0100 0.0000
## 260 0.5530 nan 0.0100 0.0000
## 280 0.5360 nan 0.0100 0.0000
## 300 0.5202 nan 0.0100 -0.0001
## 320 0.5054 nan 0.0100 0.0001
## 340 0.4907 nan 0.0100 -0.0001
## 360 0.4783 nan 0.0100 0.0000
## 380 0.4660 nan 0.0100 -0.0000
## 400 0.4545 nan 0.0100 -0.0000
## 420 0.4438 nan 0.0100 0.0001
## 440 0.4326 nan 0.0100 0.0001
## 460 0.4221 nan 0.0100 -0.0001
## 480 0.4106 nan 0.0100 -0.0002
## 500 0.4016 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0049
## 3 1.2924 nan 0.0100 0.0045
## 4 1.2832 nan 0.0100 0.0041
## 5 1.2741 nan 0.0100 0.0045
## 6 1.2655 nan 0.0100 0.0037
## 7 1.2563 nan 0.0100 0.0044
## 8 1.2469 nan 0.0100 0.0041
## 9 1.2380 nan 0.0100 0.0044
## 10 1.2293 nan 0.0100 0.0036
## 20 1.1520 nan 0.0100 0.0031
## 40 1.0261 nan 0.0100 0.0024
## 60 0.9310 nan 0.0100 0.0018
## 80 0.8580 nan 0.0100 0.0014
## 100 0.7988 nan 0.0100 0.0010
## 120 0.7502 nan 0.0100 0.0006
## 140 0.7092 nan 0.0100 0.0006
## 160 0.6761 nan 0.0100 0.0003
## 180 0.6467 nan 0.0100 0.0001
## 200 0.6212 nan 0.0100 0.0003
## 220 0.5982 nan 0.0100 0.0001
## 240 0.5767 nan 0.0100 0.0003
## 260 0.5586 nan 0.0100 0.0000
## 280 0.5403 nan 0.0100 0.0001
## 300 0.5238 nan 0.0100 0.0001
## 320 0.5094 nan 0.0100 0.0001
## 340 0.4965 nan 0.0100 0.0000
## 360 0.4830 nan 0.0100 -0.0001
## 380 0.4703 nan 0.0100 -0.0001
## 400 0.4593 nan 0.0100 -0.0000
## 420 0.4490 nan 0.0100 -0.0001
## 440 0.4372 nan 0.0100 0.0000
## 460 0.4280 nan 0.0100 -0.0001
## 480 0.4192 nan 0.0100 -0.0000
## 500 0.4096 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0047
## 2 1.3017 nan 0.0100 0.0043
## 3 1.2921 nan 0.0100 0.0046
## 4 1.2828 nan 0.0100 0.0043
## 5 1.2743 nan 0.0100 0.0040
## 6 1.2655 nan 0.0100 0.0041
## 7 1.2573 nan 0.0100 0.0038
## 8 1.2482 nan 0.0100 0.0041
## 9 1.2401 nan 0.0100 0.0037
## 10 1.2310 nan 0.0100 0.0044
## 20 1.1524 nan 0.0100 0.0035
## 40 1.0299 nan 0.0100 0.0023
## 60 0.9371 nan 0.0100 0.0017
## 80 0.8650 nan 0.0100 0.0013
## 100 0.8070 nan 0.0100 0.0009
## 120 0.7593 nan 0.0100 0.0005
## 140 0.7193 nan 0.0100 0.0004
## 160 0.6870 nan 0.0100 0.0003
## 180 0.6578 nan 0.0100 0.0005
## 200 0.6327 nan 0.0100 0.0003
## 220 0.6093 nan 0.0100 0.0005
## 240 0.5898 nan 0.0100 0.0000
## 260 0.5718 nan 0.0100 0.0002
## 280 0.5550 nan 0.0100 0.0002
## 300 0.5395 nan 0.0100 0.0000
## 320 0.5254 nan 0.0100 -0.0000
## 340 0.5120 nan 0.0100 -0.0001
## 360 0.4996 nan 0.0100 0.0001
## 380 0.4875 nan 0.0100 0.0000
## 400 0.4755 nan 0.0100 -0.0001
## 420 0.4645 nan 0.0100 -0.0000
## 440 0.4539 nan 0.0100 -0.0001
## 460 0.4441 nan 0.0100 -0.0000
## 480 0.4343 nan 0.0100 -0.0001
## 500 0.4243 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2395 nan 0.1000 0.0380
## 2 1.1673 nan 0.1000 0.0351
## 3 1.0980 nan 0.1000 0.0309
## 4 1.0441 nan 0.1000 0.0253
## 5 0.9992 nan 0.1000 0.0217
## 6 0.9546 nan 0.1000 0.0190
## 7 0.9201 nan 0.1000 0.0151
## 8 0.8905 nan 0.1000 0.0106
## 9 0.8656 nan 0.1000 0.0109
## 10 0.8411 nan 0.1000 0.0100
## 20 0.6848 nan 0.1000 0.0001
## 40 0.5461 nan 0.1000 0.0011
## 60 0.4675 nan 0.1000 -0.0009
## 80 0.4098 nan 0.1000 -0.0008
## 100 0.3573 nan 0.1000 0.0000
## 120 0.3145 nan 0.1000 -0.0011
## 140 0.2825 nan 0.1000 -0.0011
## 160 0.2582 nan 0.1000 -0.0009
## 180 0.2287 nan 0.1000 -0.0007
## 200 0.2035 nan 0.1000 -0.0002
## 220 0.1852 nan 0.1000 0.0002
## 240 0.1650 nan 0.1000 0.0001
## 260 0.1474 nan 0.1000 -0.0001
## 280 0.1345 nan 0.1000 -0.0003
## 300 0.1217 nan 0.1000 -0.0004
## 320 0.1091 nan 0.1000 -0.0002
## 340 0.0997 nan 0.1000 -0.0001
## 360 0.0902 nan 0.1000 -0.0000
## 380 0.0831 nan 0.1000 -0.0002
## 400 0.0771 nan 0.1000 -0.0002
## 420 0.0709 nan 0.1000 -0.0001
## 440 0.0649 nan 0.1000 -0.0003
## 460 0.0593 nan 0.1000 -0.0002
## 480 0.0540 nan 0.1000 -0.0002
## 500 0.0493 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2274 nan 0.1000 0.0435
## 2 1.1525 nan 0.1000 0.0338
## 3 1.0924 nan 0.1000 0.0262
## 4 1.0394 nan 0.1000 0.0218
## 5 0.9977 nan 0.1000 0.0180
## 6 0.9542 nan 0.1000 0.0183
## 7 0.9212 nan 0.1000 0.0142
## 8 0.8916 nan 0.1000 0.0113
## 9 0.8647 nan 0.1000 0.0106
## 10 0.8432 nan 0.1000 0.0076
## 20 0.6875 nan 0.1000 0.0029
## 40 0.5502 nan 0.1000 0.0004
## 60 0.4753 nan 0.1000 -0.0005
## 80 0.4122 nan 0.1000 0.0005
## 100 0.3649 nan 0.1000 -0.0008
## 120 0.3219 nan 0.1000 -0.0005
## 140 0.2866 nan 0.1000 0.0001
## 160 0.2570 nan 0.1000 -0.0008
## 180 0.2315 nan 0.1000 -0.0003
## 200 0.2066 nan 0.1000 -0.0007
## 220 0.1851 nan 0.1000 -0.0002
## 240 0.1686 nan 0.1000 -0.0006
## 260 0.1519 nan 0.1000 -0.0001
## 280 0.1368 nan 0.1000 -0.0001
## 300 0.1270 nan 0.1000 -0.0003
## 320 0.1165 nan 0.1000 -0.0002
## 340 0.1059 nan 0.1000 -0.0004
## 360 0.0962 nan 0.1000 -0.0001
## 380 0.0882 nan 0.1000 -0.0002
## 400 0.0808 nan 0.1000 -0.0002
## 420 0.0743 nan 0.1000 -0.0002
## 440 0.0675 nan 0.1000 -0.0002
## 460 0.0614 nan 0.1000 -0.0002
## 480 0.0562 nan 0.1000 -0.0001
## 500 0.0518 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0419
## 2 1.1719 nan 0.1000 0.0275
## 3 1.1045 nan 0.1000 0.0277
## 4 1.0521 nan 0.1000 0.0203
## 5 1.0015 nan 0.1000 0.0235
## 6 0.9621 nan 0.1000 0.0163
## 7 0.9296 nan 0.1000 0.0137
## 8 0.8979 nan 0.1000 0.0104
## 9 0.8701 nan 0.1000 0.0122
## 10 0.8481 nan 0.1000 0.0078
## 20 0.6911 nan 0.1000 0.0025
## 40 0.5637 nan 0.1000 -0.0013
## 60 0.4836 nan 0.1000 -0.0013
## 80 0.4253 nan 0.1000 -0.0008
## 100 0.3737 nan 0.1000 -0.0012
## 120 0.3345 nan 0.1000 -0.0005
## 140 0.2953 nan 0.1000 -0.0002
## 160 0.2664 nan 0.1000 -0.0004
## 180 0.2402 nan 0.1000 -0.0003
## 200 0.2146 nan 0.1000 -0.0005
## 220 0.1931 nan 0.1000 0.0001
## 240 0.1763 nan 0.1000 -0.0012
## 260 0.1610 nan 0.1000 -0.0006
## 280 0.1467 nan 0.1000 -0.0001
## 300 0.1344 nan 0.1000 -0.0004
## 320 0.1240 nan 0.1000 -0.0006
## 340 0.1139 nan 0.1000 -0.0003
## 360 0.1040 nan 0.1000 -0.0004
## 380 0.0966 nan 0.1000 -0.0004
## 400 0.0883 nan 0.1000 -0.0002
## 420 0.0806 nan 0.1000 -0.0005
## 440 0.0758 nan 0.1000 -0.0002
## 460 0.0702 nan 0.1000 -0.0002
## 480 0.0650 nan 0.1000 -0.0003
## 500 0.0597 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2292 nan 0.1000 0.0396
## 2 1.1522 nan 0.1000 0.0357
## 3 1.0877 nan 0.1000 0.0292
## 4 1.0292 nan 0.1000 0.0264
## 5 0.9815 nan 0.1000 0.0199
## 6 0.9429 nan 0.1000 0.0153
## 7 0.9058 nan 0.1000 0.0167
## 8 0.8730 nan 0.1000 0.0122
## 9 0.8455 nan 0.1000 0.0094
## 10 0.8191 nan 0.1000 0.0112
## 20 0.6497 nan 0.1000 0.0010
## 40 0.5048 nan 0.1000 0.0003
## 60 0.4124 nan 0.1000 -0.0006
## 80 0.3461 nan 0.1000 -0.0004
## 100 0.2924 nan 0.1000 -0.0006
## 120 0.2489 nan 0.1000 0.0001
## 140 0.2147 nan 0.1000 -0.0008
## 160 0.1885 nan 0.1000 -0.0004
## 180 0.1653 nan 0.1000 0.0001
## 200 0.1481 nan 0.1000 -0.0004
## 220 0.1308 nan 0.1000 -0.0002
## 240 0.1145 nan 0.1000 -0.0003
## 260 0.1010 nan 0.1000 -0.0000
## 280 0.0896 nan 0.1000 -0.0000
## 300 0.0800 nan 0.1000 -0.0002
## 320 0.0716 nan 0.1000 -0.0000
## 340 0.0638 nan 0.1000 -0.0002
## 360 0.0572 nan 0.1000 -0.0001
## 380 0.0512 nan 0.1000 0.0000
## 400 0.0460 nan 0.1000 -0.0000
## 420 0.0412 nan 0.1000 -0.0001
## 440 0.0374 nan 0.1000 -0.0001
## 460 0.0339 nan 0.1000 -0.0002
## 480 0.0306 nan 0.1000 -0.0000
## 500 0.0277 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2337 nan 0.1000 0.0425
## 2 1.1574 nan 0.1000 0.0374
## 3 1.0907 nan 0.1000 0.0288
## 4 1.0341 nan 0.1000 0.0262
## 5 0.9834 nan 0.1000 0.0197
## 6 0.9392 nan 0.1000 0.0185
## 7 0.9062 nan 0.1000 0.0128
## 8 0.8750 nan 0.1000 0.0114
## 9 0.8426 nan 0.1000 0.0113
## 10 0.8163 nan 0.1000 0.0075
## 20 0.6550 nan 0.1000 0.0022
## 40 0.5157 nan 0.1000 -0.0008
## 60 0.4271 nan 0.1000 -0.0006
## 80 0.3599 nan 0.1000 -0.0003
## 100 0.3088 nan 0.1000 -0.0013
## 120 0.2661 nan 0.1000 -0.0004
## 140 0.2263 nan 0.1000 -0.0006
## 160 0.1980 nan 0.1000 0.0003
## 180 0.1737 nan 0.1000 -0.0004
## 200 0.1524 nan 0.1000 -0.0008
## 220 0.1335 nan 0.1000 -0.0003
## 240 0.1188 nan 0.1000 -0.0003
## 260 0.1062 nan 0.1000 -0.0002
## 280 0.0956 nan 0.1000 -0.0005
## 300 0.0849 nan 0.1000 -0.0000
## 320 0.0756 nan 0.1000 -0.0002
## 340 0.0685 nan 0.1000 -0.0005
## 360 0.0607 nan 0.1000 -0.0003
## 380 0.0537 nan 0.1000 -0.0000
## 400 0.0483 nan 0.1000 -0.0003
## 420 0.0428 nan 0.1000 -0.0000
## 440 0.0386 nan 0.1000 -0.0001
## 460 0.0342 nan 0.1000 0.0000
## 480 0.0305 nan 0.1000 -0.0001
## 500 0.0275 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0401
## 2 1.1540 nan 0.1000 0.0344
## 3 1.0894 nan 0.1000 0.0299
## 4 1.0348 nan 0.1000 0.0241
## 5 0.9838 nan 0.1000 0.0205
## 6 0.9462 nan 0.1000 0.0163
## 7 0.9081 nan 0.1000 0.0149
## 8 0.8754 nan 0.1000 0.0144
## 9 0.8447 nan 0.1000 0.0118
## 10 0.8205 nan 0.1000 0.0082
## 20 0.6599 nan 0.1000 0.0009
## 40 0.5170 nan 0.1000 0.0007
## 60 0.4304 nan 0.1000 -0.0005
## 80 0.3627 nan 0.1000 -0.0005
## 100 0.3120 nan 0.1000 -0.0011
## 120 0.2710 nan 0.1000 -0.0011
## 140 0.2360 nan 0.1000 -0.0007
## 160 0.2071 nan 0.1000 -0.0006
## 180 0.1850 nan 0.1000 -0.0009
## 200 0.1616 nan 0.1000 -0.0004
## 220 0.1429 nan 0.1000 -0.0006
## 240 0.1260 nan 0.1000 -0.0007
## 260 0.1116 nan 0.1000 -0.0003
## 280 0.0999 nan 0.1000 -0.0000
## 300 0.0886 nan 0.1000 -0.0005
## 320 0.0794 nan 0.1000 -0.0002
## 340 0.0716 nan 0.1000 -0.0002
## 360 0.0641 nan 0.1000 -0.0002
## 380 0.0577 nan 0.1000 -0.0001
## 400 0.0514 nan 0.1000 -0.0001
## 420 0.0459 nan 0.1000 -0.0003
## 440 0.0408 nan 0.1000 -0.0001
## 460 0.0372 nan 0.1000 -0.0002
## 480 0.0333 nan 0.1000 -0.0001
## 500 0.0302 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2293 nan 0.1000 0.0461
## 2 1.1488 nan 0.1000 0.0350
## 3 1.0820 nan 0.1000 0.0293
## 4 1.0263 nan 0.1000 0.0224
## 5 0.9736 nan 0.1000 0.0245
## 6 0.9291 nan 0.1000 0.0200
## 7 0.8939 nan 0.1000 0.0143
## 8 0.8558 nan 0.1000 0.0154
## 9 0.8232 nan 0.1000 0.0118
## 10 0.7971 nan 0.1000 0.0084
## 20 0.6222 nan 0.1000 0.0035
## 40 0.4590 nan 0.1000 -0.0004
## 60 0.3666 nan 0.1000 -0.0003
## 80 0.3002 nan 0.1000 0.0002
## 100 0.2490 nan 0.1000 -0.0003
## 120 0.2071 nan 0.1000 -0.0002
## 140 0.1737 nan 0.1000 -0.0003
## 160 0.1480 nan 0.1000 -0.0007
## 180 0.1278 nan 0.1000 -0.0002
## 200 0.1088 nan 0.1000 -0.0002
## 220 0.0939 nan 0.1000 -0.0002
## 240 0.0828 nan 0.1000 -0.0003
## 260 0.0734 nan 0.1000 -0.0003
## 280 0.0644 nan 0.1000 -0.0002
## 300 0.0564 nan 0.1000 -0.0001
## 320 0.0494 nan 0.1000 -0.0000
## 340 0.0438 nan 0.1000 -0.0000
## 360 0.0382 nan 0.1000 -0.0001
## 380 0.0337 nan 0.1000 -0.0001
## 400 0.0297 nan 0.1000 -0.0000
## 420 0.0263 nan 0.1000 -0.0000
## 440 0.0230 nan 0.1000 -0.0000
## 460 0.0199 nan 0.1000 -0.0001
## 480 0.0176 nan 0.1000 -0.0000
## 500 0.0156 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0466
## 2 1.1447 nan 0.1000 0.0390
## 3 1.0775 nan 0.1000 0.0298
## 4 1.0187 nan 0.1000 0.0242
## 5 0.9669 nan 0.1000 0.0225
## 6 0.9271 nan 0.1000 0.0169
## 7 0.8912 nan 0.1000 0.0138
## 8 0.8590 nan 0.1000 0.0123
## 9 0.8215 nan 0.1000 0.0135
## 10 0.7916 nan 0.1000 0.0098
## 20 0.6172 nan 0.1000 0.0032
## 40 0.4670 nan 0.1000 0.0000
## 60 0.3744 nan 0.1000 -0.0015
## 80 0.2980 nan 0.1000 -0.0004
## 100 0.2519 nan 0.1000 -0.0008
## 120 0.2099 nan 0.1000 -0.0009
## 140 0.1771 nan 0.1000 -0.0006
## 160 0.1490 nan 0.1000 -0.0008
## 180 0.1271 nan 0.1000 -0.0003
## 200 0.1090 nan 0.1000 -0.0006
## 220 0.0946 nan 0.1000 -0.0003
## 240 0.0811 nan 0.1000 -0.0002
## 260 0.0709 nan 0.1000 -0.0002
## 280 0.0620 nan 0.1000 -0.0003
## 300 0.0541 nan 0.1000 -0.0001
## 320 0.0475 nan 0.1000 -0.0001
## 340 0.0423 nan 0.1000 -0.0002
## 360 0.0373 nan 0.1000 -0.0002
## 380 0.0327 nan 0.1000 -0.0001
## 400 0.0288 nan 0.1000 -0.0001
## 420 0.0254 nan 0.1000 -0.0001
## 440 0.0222 nan 0.1000 -0.0000
## 460 0.0195 nan 0.1000 -0.0001
## 480 0.0170 nan 0.1000 -0.0001
## 500 0.0149 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2275 nan 0.1000 0.0399
## 2 1.1516 nan 0.1000 0.0331
## 3 1.0902 nan 0.1000 0.0257
## 4 1.0345 nan 0.1000 0.0246
## 5 0.9809 nan 0.1000 0.0215
## 6 0.9368 nan 0.1000 0.0194
## 7 0.9011 nan 0.1000 0.0153
## 8 0.8678 nan 0.1000 0.0146
## 9 0.8378 nan 0.1000 0.0118
## 10 0.8092 nan 0.1000 0.0112
## 20 0.6364 nan 0.1000 0.0039
## 40 0.4870 nan 0.1000 0.0010
## 60 0.3891 nan 0.1000 0.0000
## 80 0.3211 nan 0.1000 -0.0016
## 100 0.2677 nan 0.1000 -0.0004
## 120 0.2288 nan 0.1000 -0.0010
## 140 0.1946 nan 0.1000 -0.0009
## 160 0.1654 nan 0.1000 -0.0003
## 180 0.1434 nan 0.1000 -0.0004
## 200 0.1256 nan 0.1000 -0.0003
## 220 0.1088 nan 0.1000 -0.0006
## 240 0.0959 nan 0.1000 -0.0006
## 260 0.0825 nan 0.1000 -0.0003
## 280 0.0723 nan 0.1000 -0.0003
## 300 0.0629 nan 0.1000 -0.0002
## 320 0.0561 nan 0.1000 -0.0004
## 340 0.0494 nan 0.1000 -0.0004
## 360 0.0426 nan 0.1000 -0.0001
## 380 0.0380 nan 0.1000 -0.0002
## 400 0.0333 nan 0.1000 -0.0002
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## 440 0.0257 nan 0.1000 -0.0000
## 460 0.0230 nan 0.1000 -0.0001
## 480 0.0204 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0003
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0003
## 10 1.3129 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2887 nan 0.0010 0.0004
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2586 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0003
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1669 nan 0.0010 0.0003
## 240 1.1555 nan 0.0010 0.0002
## 260 1.1443 nan 0.0010 0.0002
## 280 1.1335 nan 0.0010 0.0002
## 300 1.1227 nan 0.0010 0.0002
## 320 1.1125 nan 0.0010 0.0002
## 340 1.1026 nan 0.0010 0.0002
## 360 1.0926 nan 0.0010 0.0002
## 380 1.0833 nan 0.0010 0.0002
## 400 1.0739 nan 0.0010 0.0002
## 420 1.0651 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0477 nan 0.0010 0.0002
## 480 1.0392 nan 0.0010 0.0002
## 500 1.0310 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3048 nan 0.0010 0.0004
## 40 1.2891 nan 0.0010 0.0003
## 60 1.2740 nan 0.0010 0.0003
## 80 1.2593 nan 0.0010 0.0003
## 100 1.2451 nan 0.0010 0.0003
## 120 1.2315 nan 0.0010 0.0003
## 140 1.2181 nan 0.0010 0.0003
## 160 1.2053 nan 0.0010 0.0003
## 180 1.1930 nan 0.0010 0.0003
## 200 1.1808 nan 0.0010 0.0003
## 220 1.1688 nan 0.0010 0.0003
## 240 1.1575 nan 0.0010 0.0002
## 260 1.1460 nan 0.0010 0.0002
## 280 1.1352 nan 0.0010 0.0002
## 300 1.1246 nan 0.0010 0.0002
## 320 1.1140 nan 0.0010 0.0003
## 340 1.1038 nan 0.0010 0.0002
## 360 1.0939 nan 0.0010 0.0002
## 380 1.0843 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0658 nan 0.0010 0.0002
## 440 1.0570 nan 0.0010 0.0002
## 460 1.0482 nan 0.0010 0.0002
## 480 1.0399 nan 0.0010 0.0002
## 500 1.0318 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3188 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3163 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0004
## 9 1.3139 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3049 nan 0.0010 0.0004
## 40 1.2893 nan 0.0010 0.0003
## 60 1.2742 nan 0.0010 0.0004
## 80 1.2595 nan 0.0010 0.0003
## 100 1.2454 nan 0.0010 0.0004
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2182 nan 0.0010 0.0003
## 160 1.2055 nan 0.0010 0.0002
## 180 1.1930 nan 0.0010 0.0002
## 200 1.1808 nan 0.0010 0.0002
## 220 1.1690 nan 0.0010 0.0002
## 240 1.1574 nan 0.0010 0.0003
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## 380 1.0844 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0660 nan 0.0010 0.0002
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## 480 1.0402 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2705 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2398 nan 0.0010 0.0004
## 120 1.2246 nan 0.0010 0.0003
## 140 1.2102 nan 0.0010 0.0003
## 160 1.1964 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
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## 220 1.1573 nan 0.0010 0.0002
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## 380 1.0676 nan 0.0010 0.0002
## 400 1.0578 nan 0.0010 0.0002
## 420 1.0483 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2397 nan 0.0010 0.0003
## 120 1.2253 nan 0.0010 0.0003
## 140 1.2110 nan 0.0010 0.0003
## 160 1.1973 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
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## 280 1.1228 nan 0.0010 0.0003
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## 320 1.1006 nan 0.0010 0.0002
## 340 1.0898 nan 0.0010 0.0002
## 360 1.0796 nan 0.0010 0.0002
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## 400 1.0598 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0004
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2412 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2128 nan 0.0010 0.0003
## 160 1.1990 nan 0.0010 0.0003
## 180 1.1860 nan 0.0010 0.0003
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## 380 1.0721 nan 0.0010 0.0002
## 400 1.0623 nan 0.0010 0.0002
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## 480 1.0260 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0005
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2519 nan 0.0010 0.0003
## 100 1.2359 nan 0.0010 0.0004
## 120 1.2206 nan 0.0010 0.0003
## 140 1.2058 nan 0.0010 0.0003
## 160 1.1914 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0002
## 200 1.1638 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1382 nan 0.0010 0.0003
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## 300 1.1017 nan 0.0010 0.0002
## 320 1.0903 nan 0.0010 0.0003
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## 360 1.0682 nan 0.0010 0.0002
## 380 1.0575 nan 0.0010 0.0002
## 400 1.0471 nan 0.0010 0.0002
## 420 1.0371 nan 0.0010 0.0002
## 440 1.0272 nan 0.0010 0.0002
## 460 1.0177 nan 0.0010 0.0002
## 480 1.0086 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0005
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2521 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1911 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0003
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## 220 1.1503 nan 0.0010 0.0003
## 240 1.1375 nan 0.0010 0.0002
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## 280 1.1132 nan 0.0010 0.0003
## 300 1.1016 nan 0.0010 0.0002
## 320 1.0902 nan 0.0010 0.0003
## 340 1.0794 nan 0.0010 0.0002
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## 380 1.0583 nan 0.0010 0.0002
## 400 1.0480 nan 0.0010 0.0002
## 420 1.0382 nan 0.0010 0.0002
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## 460 1.0190 nan 0.0010 0.0002
## 480 1.0098 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0003
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2079 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1800 nan 0.0010 0.0003
## 200 1.1667 nan 0.0010 0.0003
## 220 1.1538 nan 0.0010 0.0003
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## 300 1.1051 nan 0.0010 0.0003
## 320 1.0936 nan 0.0010 0.0003
## 340 1.0827 nan 0.0010 0.0003
## 360 1.0720 nan 0.0010 0.0002
## 380 1.0617 nan 0.0010 0.0002
## 400 1.0516 nan 0.0010 0.0002
## 420 1.0418 nan 0.0010 0.0002
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## 480 1.0135 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0036
## 3 1.2965 nan 0.0100 0.0034
## 4 1.2892 nan 0.0100 0.0033
## 5 1.2812 nan 0.0100 0.0038
## 6 1.2743 nan 0.0100 0.0028
## 7 1.2668 nan 0.0100 0.0034
## 8 1.2593 nan 0.0100 0.0037
## 9 1.2515 nan 0.0100 0.0031
## 10 1.2439 nan 0.0100 0.0031
## 20 1.1793 nan 0.0100 0.0026
## 40 1.0729 nan 0.0100 0.0020
## 60 0.9926 nan 0.0100 0.0013
## 80 0.9294 nan 0.0100 0.0011
## 100 0.8778 nan 0.0100 0.0008
## 120 0.8355 nan 0.0100 0.0006
## 140 0.7990 nan 0.0100 0.0006
## 160 0.7697 nan 0.0100 0.0004
## 180 0.7434 nan 0.0100 0.0003
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## 220 0.7022 nan 0.0100 0.0002
## 240 0.6848 nan 0.0100 0.0001
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## 320 0.6281 nan 0.0100 0.0000
## 340 0.6162 nan 0.0100 -0.0000
## 360 0.6047 nan 0.0100 0.0000
## 380 0.5939 nan 0.0100 0.0000
## 400 0.5834 nan 0.0100 0.0001
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## 440 0.5644 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0038
## 4 1.2884 nan 0.0100 0.0036
## 5 1.2812 nan 0.0100 0.0033
## 6 1.2734 nan 0.0100 0.0032
## 7 1.2661 nan 0.0100 0.0030
## 8 1.2590 nan 0.0100 0.0035
## 9 1.2517 nan 0.0100 0.0031
## 10 1.2443 nan 0.0100 0.0029
## 20 1.1763 nan 0.0100 0.0026
## 40 1.0713 nan 0.0100 0.0019
## 60 0.9912 nan 0.0100 0.0016
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## 100 0.8773 nan 0.0100 0.0007
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## 180 0.7458 nan 0.0100 0.0003
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## 240 0.6886 nan 0.0100 0.0002
## 260 0.6731 nan 0.0100 0.0001
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## 300 0.6439 nan 0.0100 -0.0000
## 320 0.6316 nan 0.0100 0.0001
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## 380 0.5991 nan 0.0100 -0.0002
## 400 0.5895 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0038
## 2 1.3048 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0040
## 4 1.2876 nan 0.0100 0.0036
## 5 1.2795 nan 0.0100 0.0037
## 6 1.2724 nan 0.0100 0.0032
## 7 1.2654 nan 0.0100 0.0031
## 8 1.2585 nan 0.0100 0.0029
## 9 1.2518 nan 0.0100 0.0030
## 10 1.2447 nan 0.0100 0.0032
## 20 1.1785 nan 0.0100 0.0028
## 40 1.0727 nan 0.0100 0.0016
## 60 0.9918 nan 0.0100 0.0016
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## 120 0.8375 nan 0.0100 0.0009
## 140 0.8014 nan 0.0100 0.0005
## 160 0.7725 nan 0.0100 0.0003
## 180 0.7469 nan 0.0100 0.0001
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## 240 0.6918 nan 0.0100 0.0000
## 260 0.6776 nan 0.0100 0.0003
## 280 0.6637 nan 0.0100 0.0001
## 300 0.6506 nan 0.0100 -0.0000
## 320 0.6389 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2946 nan 0.0100 0.0035
## 4 1.2860 nan 0.0100 0.0035
## 5 1.2779 nan 0.0100 0.0038
## 6 1.2696 nan 0.0100 0.0034
## 7 1.2614 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0031
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## 20 1.1704 nan 0.0100 0.0030
## 40 1.0566 nan 0.0100 0.0020
## 60 0.9691 nan 0.0100 0.0018
## 80 0.9043 nan 0.0100 0.0013
## 100 0.8500 nan 0.0100 0.0009
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## 280 0.6150 nan 0.0100 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2941 nan 0.0100 0.0036
## 4 1.2861 nan 0.0100 0.0035
## 5 1.2779 nan 0.0100 0.0036
## 6 1.2696 nan 0.0100 0.0037
## 7 1.2620 nan 0.0100 0.0034
## 8 1.2542 nan 0.0100 0.0031
## 9 1.2464 nan 0.0100 0.0035
## 10 1.2391 nan 0.0100 0.0033
## 20 1.1709 nan 0.0100 0.0029
## 40 1.0586 nan 0.0100 0.0022
## 60 0.9755 nan 0.0100 0.0014
## 80 0.9072 nan 0.0100 0.0007
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## 160 0.7424 nan 0.0100 0.0003
## 180 0.7162 nan 0.0100 0.0004
## 200 0.6923 nan 0.0100 -0.0002
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## 320 0.5929 nan 0.0100 -0.0000
## 340 0.5792 nan 0.0100 0.0000
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## 380 0.5552 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3041 nan 0.0100 0.0034
## 3 1.2954 nan 0.0100 0.0035
## 4 1.2876 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0040
## 6 1.2715 nan 0.0100 0.0031
## 7 1.2636 nan 0.0100 0.0035
## 8 1.2556 nan 0.0100 0.0033
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## 10 1.2411 nan 0.0100 0.0029
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## 40 1.0607 nan 0.0100 0.0020
## 60 0.9751 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0041
## 2 1.3028 nan 0.0100 0.0036
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2848 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0039
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2591 nan 0.0100 0.0035
## 8 1.2511 nan 0.0100 0.0034
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## 40 1.0462 nan 0.0100 0.0024
## 60 0.9582 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0039
## 2 1.3039 nan 0.0100 0.0040
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2849 nan 0.0100 0.0041
## 5 1.2759 nan 0.0100 0.0040
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2590 nan 0.0100 0.0039
## 8 1.2510 nan 0.0100 0.0033
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## 20 1.1629 nan 0.0100 0.0030
## 40 1.0474 nan 0.0100 0.0021
## 60 0.9585 nan 0.0100 0.0015
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## 380 0.5161 nan 0.0100 -0.0001
## 400 0.5045 nan 0.0100 -0.0001
## 420 0.4925 nan 0.0100 0.0001
## 440 0.4821 nan 0.0100 -0.0002
## 460 0.4715 nan 0.0100 -0.0000
## 480 0.4617 nan 0.0100 -0.0001
## 500 0.4511 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0039
## 2 1.3036 nan 0.0100 0.0035
## 3 1.2950 nan 0.0100 0.0043
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2777 nan 0.0100 0.0039
## 6 1.2694 nan 0.0100 0.0036
## 7 1.2604 nan 0.0100 0.0040
## 8 1.2527 nan 0.0100 0.0034
## 9 1.2454 nan 0.0100 0.0032
## 10 1.2383 nan 0.0100 0.0034
## 20 1.1685 nan 0.0100 0.0025
## 40 1.0529 nan 0.0100 0.0022
## 60 0.9639 nan 0.0100 0.0016
## 80 0.8954 nan 0.0100 0.0012
## 100 0.8410 nan 0.0100 0.0009
## 120 0.7926 nan 0.0100 0.0009
## 140 0.7562 nan 0.0100 0.0008
## 160 0.7249 nan 0.0100 0.0005
## 180 0.6976 nan 0.0100 0.0003
## 200 0.6732 nan 0.0100 0.0003
## 220 0.6512 nan 0.0100 0.0000
## 240 0.6313 nan 0.0100 0.0002
## 260 0.6129 nan 0.0100 0.0000
## 280 0.5961 nan 0.0100 0.0003
## 300 0.5809 nan 0.0100 0.0001
## 320 0.5670 nan 0.0100 0.0001
## 340 0.5545 nan 0.0100 0.0000
## 360 0.5417 nan 0.0100 -0.0001
## 380 0.5293 nan 0.0100 0.0001
## 400 0.5175 nan 0.0100 -0.0002
## 420 0.5068 nan 0.0100 -0.0000
## 440 0.4966 nan 0.0100 0.0001
## 460 0.4866 nan 0.0100 0.0001
## 480 0.4770 nan 0.0100 -0.0002
## 500 0.4683 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0351
## 2 1.1721 nan 0.1000 0.0326
## 3 1.1215 nan 0.1000 0.0251
## 4 1.0710 nan 0.1000 0.0240
## 5 1.0276 nan 0.1000 0.0167
## 6 0.9915 nan 0.1000 0.0154
## 7 0.9602 nan 0.1000 0.0142
## 8 0.9274 nan 0.1000 0.0128
## 9 0.9027 nan 0.1000 0.0100
## 10 0.8817 nan 0.1000 0.0068
## 20 0.7310 nan 0.1000 0.0004
## 40 0.5952 nan 0.1000 0.0018
## 60 0.5108 nan 0.1000 0.0006
## 80 0.4454 nan 0.1000 -0.0002
## 100 0.3963 nan 0.1000 -0.0004
## 120 0.3522 nan 0.1000 0.0001
## 140 0.3156 nan 0.1000 -0.0002
## 160 0.2874 nan 0.1000 -0.0013
## 180 0.2574 nan 0.1000 -0.0007
## 200 0.2339 nan 0.1000 -0.0008
## 220 0.2108 nan 0.1000 -0.0005
## 240 0.1919 nan 0.1000 -0.0000
## 260 0.1750 nan 0.1000 -0.0001
## 280 0.1591 nan 0.1000 -0.0000
## 300 0.1461 nan 0.1000 -0.0005
## 320 0.1349 nan 0.1000 -0.0000
## 340 0.1243 nan 0.1000 -0.0000
## 360 0.1135 nan 0.1000 -0.0001
## 380 0.1045 nan 0.1000 -0.0004
## 400 0.0972 nan 0.1000 -0.0001
## 420 0.0892 nan 0.1000 -0.0002
## 440 0.0822 nan 0.1000 -0.0002
## 460 0.0754 nan 0.1000 -0.0001
## 480 0.0692 nan 0.1000 -0.0000
## 500 0.0642 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2456 nan 0.1000 0.0342
## 2 1.1787 nan 0.1000 0.0306
## 3 1.1188 nan 0.1000 0.0276
## 4 1.0739 nan 0.1000 0.0175
## 5 1.0296 nan 0.1000 0.0192
## 6 0.9899 nan 0.1000 0.0164
## 7 0.9560 nan 0.1000 0.0141
## 8 0.9268 nan 0.1000 0.0111
## 9 0.8987 nan 0.1000 0.0129
## 10 0.8775 nan 0.1000 0.0080
## 20 0.7242 nan 0.1000 0.0008
## 40 0.5901 nan 0.1000 0.0015
## 60 0.5084 nan 0.1000 -0.0002
## 80 0.4478 nan 0.1000 -0.0008
## 100 0.3931 nan 0.1000 -0.0015
## 120 0.3543 nan 0.1000 -0.0001
## 140 0.3128 nan 0.1000 -0.0008
## 160 0.2826 nan 0.1000 -0.0003
## 180 0.2567 nan 0.1000 -0.0009
## 200 0.2328 nan 0.1000 0.0002
## 220 0.2134 nan 0.1000 -0.0001
## 240 0.1934 nan 0.1000 -0.0003
## 260 0.1782 nan 0.1000 -0.0004
## 280 0.1611 nan 0.1000 -0.0003
## 300 0.1487 nan 0.1000 -0.0001
## 320 0.1345 nan 0.1000 -0.0002
## 340 0.1233 nan 0.1000 -0.0005
## 360 0.1132 nan 0.1000 -0.0005
## 380 0.1034 nan 0.1000 -0.0003
## 400 0.0948 nan 0.1000 -0.0002
## 420 0.0865 nan 0.1000 -0.0001
## 440 0.0799 nan 0.1000 -0.0001
## 460 0.0746 nan 0.1000 -0.0002
## 480 0.0691 nan 0.1000 -0.0002
## 500 0.0641 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0338
## 2 1.1772 nan 0.1000 0.0289
## 3 1.1188 nan 0.1000 0.0242
## 4 1.0690 nan 0.1000 0.0201
## 5 1.0274 nan 0.1000 0.0172
## 6 0.9891 nan 0.1000 0.0146
## 7 0.9611 nan 0.1000 0.0108
## 8 0.9276 nan 0.1000 0.0133
## 9 0.8999 nan 0.1000 0.0107
## 10 0.8758 nan 0.1000 0.0086
## 20 0.7266 nan 0.1000 0.0034
## 40 0.5911 nan 0.1000 -0.0003
## 60 0.5180 nan 0.1000 -0.0023
## 80 0.4554 nan 0.1000 0.0001
## 100 0.4065 nan 0.1000 -0.0014
## 120 0.3608 nan 0.1000 -0.0010
## 140 0.3246 nan 0.1000 -0.0003
## 160 0.2956 nan 0.1000 -0.0011
## 180 0.2648 nan 0.1000 -0.0017
## 200 0.2383 nan 0.1000 -0.0011
## 220 0.2173 nan 0.1000 -0.0005
## 240 0.1999 nan 0.1000 -0.0012
## 260 0.1847 nan 0.1000 -0.0003
## 280 0.1688 nan 0.1000 0.0001
## 300 0.1544 nan 0.1000 -0.0009
## 320 0.1421 nan 0.1000 -0.0005
## 340 0.1323 nan 0.1000 -0.0006
## 360 0.1228 nan 0.1000 -0.0001
## 380 0.1149 nan 0.1000 -0.0004
## 400 0.1070 nan 0.1000 -0.0006
## 420 0.0988 nan 0.1000 -0.0005
## 440 0.0914 nan 0.1000 -0.0004
## 460 0.0839 nan 0.1000 -0.0003
## 480 0.0772 nan 0.1000 -0.0001
## 500 0.0715 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0391
## 2 1.1663 nan 0.1000 0.0317
## 3 1.1084 nan 0.1000 0.0233
## 4 1.0592 nan 0.1000 0.0195
## 5 1.0149 nan 0.1000 0.0164
## 6 0.9768 nan 0.1000 0.0161
## 7 0.9421 nan 0.1000 0.0130
## 8 0.9090 nan 0.1000 0.0120
## 9 0.8806 nan 0.1000 0.0087
## 10 0.8567 nan 0.1000 0.0070
## 20 0.6846 nan 0.1000 0.0036
## 40 0.5441 nan 0.1000 0.0007
## 60 0.4508 nan 0.1000 0.0007
## 80 0.3771 nan 0.1000 -0.0012
## 100 0.3274 nan 0.1000 0.0008
## 120 0.2859 nan 0.1000 -0.0002
## 140 0.2516 nan 0.1000 -0.0010
## 160 0.2189 nan 0.1000 -0.0010
## 180 0.1943 nan 0.1000 -0.0005
## 200 0.1727 nan 0.1000 -0.0003
## 220 0.1519 nan 0.1000 0.0000
## 240 0.1345 nan 0.1000 -0.0001
## 260 0.1211 nan 0.1000 -0.0004
## 280 0.1083 nan 0.1000 -0.0002
## 300 0.0967 nan 0.1000 -0.0001
## 320 0.0867 nan 0.1000 -0.0001
## 340 0.0781 nan 0.1000 -0.0005
## 360 0.0709 nan 0.1000 -0.0004
## 380 0.0640 nan 0.1000 -0.0001
## 400 0.0582 nan 0.1000 -0.0003
## 420 0.0528 nan 0.1000 -0.0000
## 440 0.0482 nan 0.1000 -0.0001
## 460 0.0433 nan 0.1000 -0.0000
## 480 0.0392 nan 0.1000 -0.0002
## 500 0.0356 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0364
## 2 1.1677 nan 0.1000 0.0318
## 3 1.1084 nan 0.1000 0.0275
## 4 1.0541 nan 0.1000 0.0215
## 5 1.0095 nan 0.1000 0.0181
## 6 0.9688 nan 0.1000 0.0145
## 7 0.9325 nan 0.1000 0.0170
## 8 0.8998 nan 0.1000 0.0152
## 9 0.8723 nan 0.1000 0.0089
## 10 0.8443 nan 0.1000 0.0117
## 20 0.6886 nan 0.1000 0.0006
## 40 0.5434 nan 0.1000 -0.0000
## 60 0.4573 nan 0.1000 -0.0001
## 80 0.3887 nan 0.1000 -0.0003
## 100 0.3354 nan 0.1000 -0.0005
## 120 0.2908 nan 0.1000 -0.0007
## 140 0.2574 nan 0.1000 -0.0008
## 160 0.2263 nan 0.1000 -0.0007
## 180 0.1987 nan 0.1000 -0.0010
## 200 0.1758 nan 0.1000 -0.0010
## 220 0.1552 nan 0.1000 -0.0006
## 240 0.1358 nan 0.1000 0.0001
## 260 0.1226 nan 0.1000 -0.0000
## 280 0.1085 nan 0.1000 -0.0004
## 300 0.0978 nan 0.1000 -0.0002
## 320 0.0890 nan 0.1000 -0.0006
## 340 0.0814 nan 0.1000 -0.0001
## 360 0.0731 nan 0.1000 -0.0002
## 380 0.0661 nan 0.1000 -0.0002
## 400 0.0600 nan 0.1000 -0.0003
## 420 0.0548 nan 0.1000 -0.0001
## 440 0.0491 nan 0.1000 -0.0003
## 460 0.0441 nan 0.1000 0.0000
## 480 0.0402 nan 0.1000 -0.0002
## 500 0.0361 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0407
## 2 1.1613 nan 0.1000 0.0310
## 3 1.1051 nan 0.1000 0.0272
## 4 1.0503 nan 0.1000 0.0237
## 5 1.0038 nan 0.1000 0.0191
## 6 0.9637 nan 0.1000 0.0145
## 7 0.9318 nan 0.1000 0.0140
## 8 0.8990 nan 0.1000 0.0148
## 9 0.8759 nan 0.1000 0.0076
## 10 0.8523 nan 0.1000 0.0094
## 20 0.7075 nan 0.1000 0.0007
## 40 0.5638 nan 0.1000 -0.0001
## 60 0.4784 nan 0.1000 0.0004
## 80 0.4104 nan 0.1000 -0.0011
## 100 0.3529 nan 0.1000 -0.0009
## 120 0.3083 nan 0.1000 -0.0006
## 140 0.2743 nan 0.1000 -0.0012
## 160 0.2436 nan 0.1000 -0.0010
## 180 0.2199 nan 0.1000 -0.0000
## 200 0.1953 nan 0.1000 -0.0011
## 220 0.1744 nan 0.1000 -0.0003
## 240 0.1573 nan 0.1000 -0.0004
## 260 0.1402 nan 0.1000 -0.0008
## 280 0.1257 nan 0.1000 -0.0001
## 300 0.1128 nan 0.1000 -0.0002
## 320 0.1028 nan 0.1000 -0.0004
## 340 0.0928 nan 0.1000 -0.0004
## 360 0.0835 nan 0.1000 -0.0004
## 380 0.0757 nan 0.1000 -0.0004
## 400 0.0688 nan 0.1000 -0.0001
## 420 0.0623 nan 0.1000 -0.0002
## 440 0.0571 nan 0.1000 -0.0002
## 460 0.0525 nan 0.1000 -0.0001
## 480 0.0477 nan 0.1000 -0.0002
## 500 0.0439 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2383 nan 0.1000 0.0387
## 2 1.1579 nan 0.1000 0.0360
## 3 1.0965 nan 0.1000 0.0285
## 4 1.0450 nan 0.1000 0.0221
## 5 0.9962 nan 0.1000 0.0205
## 6 0.9571 nan 0.1000 0.0152
## 7 0.9173 nan 0.1000 0.0166
## 8 0.8795 nan 0.1000 0.0130
## 9 0.8487 nan 0.1000 0.0117
## 10 0.8193 nan 0.1000 0.0100
## 20 0.6546 nan 0.1000 0.0034
## 40 0.4944 nan 0.1000 0.0020
## 60 0.3997 nan 0.1000 -0.0004
## 80 0.3364 nan 0.1000 -0.0002
## 100 0.2802 nan 0.1000 -0.0005
## 120 0.2370 nan 0.1000 -0.0005
## 140 0.2057 nan 0.1000 -0.0011
## 160 0.1753 nan 0.1000 -0.0000
## 180 0.1527 nan 0.1000 -0.0009
## 200 0.1308 nan 0.1000 -0.0002
## 220 0.1142 nan 0.1000 -0.0004
## 240 0.1005 nan 0.1000 -0.0001
## 260 0.0892 nan 0.1000 -0.0001
## 280 0.0786 nan 0.1000 -0.0001
## 300 0.0702 nan 0.1000 -0.0002
## 320 0.0610 nan 0.1000 -0.0000
## 340 0.0542 nan 0.1000 -0.0001
## 360 0.0478 nan 0.1000 -0.0001
## 380 0.0420 nan 0.1000 -0.0001
## 400 0.0372 nan 0.1000 -0.0002
## 420 0.0331 nan 0.1000 -0.0001
## 440 0.0296 nan 0.1000 0.0000
## 460 0.0264 nan 0.1000 -0.0000
## 480 0.0234 nan 0.1000 0.0000
## 500 0.0211 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0367
## 2 1.1644 nan 0.1000 0.0303
## 3 1.0983 nan 0.1000 0.0302
## 4 1.0389 nan 0.1000 0.0248
## 5 0.9880 nan 0.1000 0.0219
## 6 0.9463 nan 0.1000 0.0163
## 7 0.9142 nan 0.1000 0.0131
## 8 0.8792 nan 0.1000 0.0135
## 9 0.8531 nan 0.1000 0.0093
## 10 0.8281 nan 0.1000 0.0081
## 20 0.6629 nan 0.1000 0.0016
## 40 0.5090 nan 0.1000 -0.0013
## 60 0.4115 nan 0.1000 -0.0011
## 80 0.3411 nan 0.1000 -0.0022
## 100 0.2831 nan 0.1000 -0.0011
## 120 0.2416 nan 0.1000 -0.0016
## 140 0.2082 nan 0.1000 -0.0007
## 160 0.1770 nan 0.1000 -0.0006
## 180 0.1530 nan 0.1000 -0.0008
## 200 0.1337 nan 0.1000 -0.0004
## 220 0.1144 nan 0.1000 -0.0001
## 240 0.1003 nan 0.1000 -0.0004
## 260 0.0876 nan 0.1000 0.0001
## 280 0.0773 nan 0.1000 -0.0003
## 300 0.0686 nan 0.1000 -0.0004
## 320 0.0612 nan 0.1000 -0.0003
## 340 0.0537 nan 0.1000 -0.0001
## 360 0.0475 nan 0.1000 -0.0002
## 380 0.0419 nan 0.1000 -0.0002
## 400 0.0371 nan 0.1000 -0.0002
## 420 0.0327 nan 0.1000 -0.0001
## 440 0.0291 nan 0.1000 -0.0001
## 460 0.0259 nan 0.1000 -0.0001
## 480 0.0231 nan 0.1000 -0.0002
## 500 0.0203 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2416 nan 0.1000 0.0364
## 2 1.1654 nan 0.1000 0.0367
## 3 1.0994 nan 0.1000 0.0257
## 4 1.0472 nan 0.1000 0.0212
## 5 1.0013 nan 0.1000 0.0192
## 6 0.9615 nan 0.1000 0.0153
## 7 0.9210 nan 0.1000 0.0178
## 8 0.8936 nan 0.1000 0.0112
## 9 0.8646 nan 0.1000 0.0127
## 10 0.8405 nan 0.1000 0.0097
## 20 0.6775 nan 0.1000 0.0012
## 40 0.5218 nan 0.1000 0.0004
## 60 0.4265 nan 0.1000 -0.0006
## 80 0.3591 nan 0.1000 -0.0014
## 100 0.3051 nan 0.1000 -0.0010
## 120 0.2610 nan 0.1000 -0.0019
## 140 0.2205 nan 0.1000 -0.0015
## 160 0.1930 nan 0.1000 -0.0007
## 180 0.1687 nan 0.1000 -0.0006
## 200 0.1450 nan 0.1000 -0.0004
## 220 0.1280 nan 0.1000 -0.0002
## 240 0.1122 nan 0.1000 -0.0006
## 260 0.1001 nan 0.1000 -0.0002
## 280 0.0879 nan 0.1000 -0.0004
## 300 0.0778 nan 0.1000 -0.0002
## 320 0.0686 nan 0.1000 -0.0004
## 340 0.0604 nan 0.1000 -0.0004
## 360 0.0542 nan 0.1000 -0.0003
## 380 0.0482 nan 0.1000 -0.0003
## 400 0.0428 nan 0.1000 -0.0002
## 420 0.0384 nan 0.1000 -0.0002
## 440 0.0343 nan 0.1000 -0.0001
## 460 0.0312 nan 0.1000 -0.0001
## 480 0.0276 nan 0.1000 -0.0002
## 500 0.0244 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0003
## 100 1.2405 nan 0.0010 0.0004
## 120 1.2263 nan 0.0010 0.0002
## 140 1.2125 nan 0.0010 0.0003
## 160 1.1990 nan 0.0010 0.0003
## 180 1.1861 nan 0.0010 0.0003
## 200 1.1732 nan 0.0010 0.0002
## 220 1.1611 nan 0.0010 0.0002
## 240 1.1491 nan 0.0010 0.0002
## 260 1.1374 nan 0.0010 0.0003
## 280 1.1261 nan 0.0010 0.0003
## 300 1.1153 nan 0.0010 0.0002
## 320 1.1045 nan 0.0010 0.0002
## 340 1.0940 nan 0.0010 0.0002
## 360 1.0840 nan 0.0010 0.0002
## 380 1.0742 nan 0.0010 0.0002
## 400 1.0646 nan 0.0010 0.0002
## 420 1.0552 nan 0.0010 0.0002
## 440 1.0461 nan 0.0010 0.0002
## 460 1.0374 nan 0.0010 0.0002
## 480 1.0288 nan 0.0010 0.0002
## 500 1.0202 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0003
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0003
## 80 1.2552 nan 0.0010 0.0004
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2258 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
## 160 1.1981 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1597 nan 0.0010 0.0003
## 240 1.1480 nan 0.0010 0.0002
## 260 1.1363 nan 0.0010 0.0003
## 280 1.1251 nan 0.0010 0.0002
## 300 1.1142 nan 0.0010 0.0002
## 320 1.1038 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0837 nan 0.0010 0.0002
## 380 1.0739 nan 0.0010 0.0002
## 400 1.0645 nan 0.0010 0.0002
## 420 1.0553 nan 0.0010 0.0002
## 440 1.0463 nan 0.0010 0.0002
## 460 1.0376 nan 0.0010 0.0002
## 480 1.0290 nan 0.0010 0.0002
## 500 1.0208 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0003
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0003
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2712 nan 0.0010 0.0004
## 80 1.2559 nan 0.0010 0.0003
## 100 1.2408 nan 0.0010 0.0003
## 120 1.2264 nan 0.0010 0.0003
## 140 1.2126 nan 0.0010 0.0003
## 160 1.1992 nan 0.0010 0.0002
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1733 nan 0.0010 0.0003
## 220 1.1612 nan 0.0010 0.0003
## 240 1.1494 nan 0.0010 0.0003
## 260 1.1381 nan 0.0010 0.0003
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1165 nan 0.0010 0.0003
## 320 1.1060 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0859 nan 0.0010 0.0002
## 380 1.0763 nan 0.0010 0.0002
## 400 1.0671 nan 0.0010 0.0002
## 420 1.0581 nan 0.0010 0.0002
## 440 1.0495 nan 0.0010 0.0002
## 460 1.0407 nan 0.0010 0.0002
## 480 1.0323 nan 0.0010 0.0002
## 500 1.0243 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0003
## 100 1.2359 nan 0.0010 0.0003
## 120 1.2205 nan 0.0010 0.0003
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0003
## 200 1.1642 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
## 260 1.1265 nan 0.0010 0.0002
## 280 1.1149 nan 0.0010 0.0003
## 300 1.1034 nan 0.0010 0.0003
## 320 1.0921 nan 0.0010 0.0003
## 340 1.0811 nan 0.0010 0.0002
## 360 1.0706 nan 0.0010 0.0002
## 380 1.0602 nan 0.0010 0.0003
## 400 1.0503 nan 0.0010 0.0002
## 420 1.0405 nan 0.0010 0.0002
## 440 1.0308 nan 0.0010 0.0002
## 460 1.0213 nan 0.0010 0.0002
## 480 1.0125 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0004
## 100 1.2364 nan 0.0010 0.0003
## 120 1.2213 nan 0.0010 0.0003
## 140 1.2066 nan 0.0010 0.0003
## 160 1.1926 nan 0.0010 0.0003
## 180 1.1787 nan 0.0010 0.0003
## 200 1.1655 nan 0.0010 0.0003
## 220 1.1526 nan 0.0010 0.0003
## 240 1.1399 nan 0.0010 0.0003
## 260 1.1279 nan 0.0010 0.0003
## 280 1.1160 nan 0.0010 0.0002
## 300 1.1047 nan 0.0010 0.0002
## 320 1.0937 nan 0.0010 0.0002
## 340 1.0829 nan 0.0010 0.0002
## 360 1.0726 nan 0.0010 0.0002
## 380 1.0625 nan 0.0010 0.0002
## 400 1.0526 nan 0.0010 0.0002
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## 480 1.0153 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0005
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2377 nan 0.0010 0.0003
## 120 1.2225 nan 0.0010 0.0004
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1678 nan 0.0010 0.0003
## 220 1.1549 nan 0.0010 0.0002
## 240 1.1428 nan 0.0010 0.0002
## 260 1.1306 nan 0.0010 0.0003
## 280 1.1188 nan 0.0010 0.0003
## 300 1.1075 nan 0.0010 0.0003
## 320 1.0964 nan 0.0010 0.0002
## 340 1.0858 nan 0.0010 0.0002
## 360 1.0754 nan 0.0010 0.0002
## 380 1.0652 nan 0.0010 0.0002
## 400 1.0554 nan 0.0010 0.0002
## 420 1.0456 nan 0.0010 0.0002
## 440 1.0365 nan 0.0010 0.0002
## 460 1.0275 nan 0.0010 0.0002
## 480 1.0185 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3016 nan 0.0010 0.0005
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2656 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0003
## 100 1.2320 nan 0.0010 0.0003
## 120 1.2161 nan 0.0010 0.0003
## 140 1.2006 nan 0.0010 0.0004
## 160 1.1857 nan 0.0010 0.0003
## 180 1.1714 nan 0.0010 0.0003
## 200 1.1577 nan 0.0010 0.0002
## 220 1.1444 nan 0.0010 0.0003
## 240 1.1314 nan 0.0010 0.0003
## 260 1.1188 nan 0.0010 0.0003
## 280 1.1065 nan 0.0010 0.0003
## 300 1.0947 nan 0.0010 0.0003
## 320 1.0830 nan 0.0010 0.0003
## 340 1.0718 nan 0.0010 0.0002
## 360 1.0609 nan 0.0010 0.0002
## 380 1.0501 nan 0.0010 0.0002
## 400 1.0397 nan 0.0010 0.0002
## 420 1.0297 nan 0.0010 0.0002
## 440 1.0197 nan 0.0010 0.0002
## 460 1.0101 nan 0.0010 0.0002
## 480 1.0008 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2332 nan 0.0010 0.0004
## 120 1.2172 nan 0.0010 0.0003
## 140 1.2021 nan 0.0010 0.0003
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1452 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1195 nan 0.0010 0.0003
## 280 1.1073 nan 0.0010 0.0002
## 300 1.0956 nan 0.0010 0.0002
## 320 1.0843 nan 0.0010 0.0003
## 340 1.0729 nan 0.0010 0.0003
## 360 1.0618 nan 0.0010 0.0002
## 380 1.0515 nan 0.0010 0.0002
## 400 1.0412 nan 0.0010 0.0002
## 420 1.0311 nan 0.0010 0.0002
## 440 1.0215 nan 0.0010 0.0001
## 460 1.0120 nan 0.0010 0.0002
## 480 1.0029 nan 0.0010 0.0002
## 500 0.9940 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0003
## 80 1.2510 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0004
## 120 1.2197 nan 0.0010 0.0004
## 140 1.2046 nan 0.0010 0.0003
## 160 1.1899 nan 0.0010 0.0003
## 180 1.1760 nan 0.0010 0.0003
## 200 1.1623 nan 0.0010 0.0003
## 220 1.1493 nan 0.0010 0.0003
## 240 1.1363 nan 0.0010 0.0003
## 260 1.1237 nan 0.0010 0.0003
## 280 1.1116 nan 0.0010 0.0002
## 300 1.0997 nan 0.0010 0.0002
## 320 1.0886 nan 0.0010 0.0003
## 340 1.0776 nan 0.0010 0.0003
## 360 1.0668 nan 0.0010 0.0002
## 380 1.0564 nan 0.0010 0.0002
## 400 1.0464 nan 0.0010 0.0002
## 420 1.0366 nan 0.0010 0.0002
## 440 1.0269 nan 0.0010 0.0002
## 460 1.0175 nan 0.0010 0.0002
## 480 1.0083 nan 0.0010 0.0002
## 500 0.9994 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3027 nan 0.0100 0.0039
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2864 nan 0.0100 0.0038
## 5 1.2783 nan 0.0100 0.0032
## 6 1.2699 nan 0.0100 0.0033
## 7 1.2623 nan 0.0100 0.0039
## 8 1.2551 nan 0.0100 0.0034
## 9 1.2479 nan 0.0100 0.0033
## 10 1.2400 nan 0.0100 0.0037
## 20 1.1710 nan 0.0100 0.0027
## 40 1.0651 nan 0.0100 0.0018
## 60 0.9848 nan 0.0100 0.0012
## 80 0.9193 nan 0.0100 0.0013
## 100 0.8687 nan 0.0100 0.0010
## 120 0.8263 nan 0.0100 0.0006
## 140 0.7923 nan 0.0100 0.0006
## 160 0.7643 nan 0.0100 0.0005
## 180 0.7391 nan 0.0100 0.0003
## 200 0.7170 nan 0.0100 0.0002
## 220 0.6980 nan 0.0100 -0.0000
## 240 0.6803 nan 0.0100 0.0002
## 260 0.6641 nan 0.0100 0.0001
## 280 0.6508 nan 0.0100 -0.0001
## 300 0.6387 nan 0.0100 0.0000
## 320 0.6273 nan 0.0100 0.0001
## 340 0.6168 nan 0.0100 0.0000
## 360 0.6058 nan 0.0100 0.0001
## 380 0.5946 nan 0.0100 0.0000
## 400 0.5856 nan 0.0100 -0.0001
## 420 0.5756 nan 0.0100 -0.0000
## 440 0.5669 nan 0.0100 -0.0001
## 460 0.5580 nan 0.0100 -0.0001
## 480 0.5495 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0043
## 2 1.3032 nan 0.0100 0.0038
## 3 1.2945 nan 0.0100 0.0037
## 4 1.2872 nan 0.0100 0.0032
## 5 1.2789 nan 0.0100 0.0041
## 6 1.2705 nan 0.0100 0.0038
## 7 1.2630 nan 0.0100 0.0033
## 8 1.2550 nan 0.0100 0.0034
## 9 1.2472 nan 0.0100 0.0035
## 10 1.2393 nan 0.0100 0.0033
## 20 1.1710 nan 0.0100 0.0025
## 40 1.0627 nan 0.0100 0.0020
## 60 0.9811 nan 0.0100 0.0014
## 80 0.9158 nan 0.0100 0.0011
## 100 0.8653 nan 0.0100 0.0010
## 120 0.8230 nan 0.0100 0.0007
## 140 0.7888 nan 0.0100 0.0006
## 160 0.7590 nan 0.0100 0.0005
## 180 0.7343 nan 0.0100 0.0003
## 200 0.7122 nan 0.0100 0.0002
## 220 0.6925 nan 0.0100 0.0000
## 240 0.6765 nan 0.0100 0.0001
## 260 0.6619 nan 0.0100 -0.0001
## 280 0.6476 nan 0.0100 0.0001
## 300 0.6346 nan 0.0100 0.0001
## 320 0.6238 nan 0.0100 -0.0001
## 340 0.6125 nan 0.0100 -0.0001
## 360 0.6015 nan 0.0100 0.0000
## 380 0.5917 nan 0.0100 -0.0000
## 400 0.5828 nan 0.0100 -0.0001
## 420 0.5741 nan 0.0100 0.0000
## 440 0.5648 nan 0.0100 -0.0000
## 460 0.5560 nan 0.0100 -0.0001
## 480 0.5474 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0042
## 2 1.3035 nan 0.0100 0.0042
## 3 1.2954 nan 0.0100 0.0037
## 4 1.2871 nan 0.0100 0.0038
## 5 1.2793 nan 0.0100 0.0037
## 6 1.2719 nan 0.0100 0.0034
## 7 1.2641 nan 0.0100 0.0035
## 8 1.2569 nan 0.0100 0.0034
## 9 1.2493 nan 0.0100 0.0033
## 10 1.2419 nan 0.0100 0.0032
## 20 1.1753 nan 0.0100 0.0029
## 40 1.0657 nan 0.0100 0.0022
## 60 0.9821 nan 0.0100 0.0015
## 80 0.9192 nan 0.0100 0.0013
## 100 0.8684 nan 0.0100 0.0006
## 120 0.8274 nan 0.0100 0.0007
## 140 0.7933 nan 0.0100 0.0004
## 160 0.7649 nan 0.0100 0.0003
## 180 0.7402 nan 0.0100 0.0004
## 200 0.7187 nan 0.0100 0.0002
## 220 0.7001 nan 0.0100 0.0001
## 240 0.6851 nan 0.0100 0.0002
## 260 0.6692 nan 0.0100 -0.0000
## 280 0.6565 nan 0.0100 0.0001
## 300 0.6442 nan 0.0100 0.0001
## 320 0.6327 nan 0.0100 -0.0001
## 340 0.6217 nan 0.0100 -0.0001
## 360 0.6109 nan 0.0100 -0.0001
## 380 0.6015 nan 0.0100 0.0001
## 400 0.5917 nan 0.0100 0.0000
## 420 0.5828 nan 0.0100 -0.0000
## 440 0.5739 nan 0.0100 -0.0001
## 460 0.5644 nan 0.0100 -0.0002
## 480 0.5572 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0042
## 2 1.3023 nan 0.0100 0.0040
## 3 1.2930 nan 0.0100 0.0041
## 4 1.2849 nan 0.0100 0.0036
## 5 1.2762 nan 0.0100 0.0037
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2601 nan 0.0100 0.0038
## 8 1.2521 nan 0.0100 0.0037
## 9 1.2444 nan 0.0100 0.0039
## 10 1.2367 nan 0.0100 0.0033
## 20 1.1665 nan 0.0100 0.0032
## 40 1.0525 nan 0.0100 0.0020
## 60 0.9674 nan 0.0100 0.0015
## 80 0.8981 nan 0.0100 0.0014
## 100 0.8441 nan 0.0100 0.0007
## 120 0.8011 nan 0.0100 0.0005
## 140 0.7648 nan 0.0100 0.0005
## 160 0.7328 nan 0.0100 0.0003
## 180 0.7061 nan 0.0100 0.0002
## 200 0.6819 nan 0.0100 0.0003
## 220 0.6605 nan 0.0100 0.0002
## 240 0.6417 nan 0.0100 0.0003
## 260 0.6248 nan 0.0100 -0.0000
## 280 0.6089 nan 0.0100 0.0001
## 300 0.5962 nan 0.0100 -0.0002
## 320 0.5834 nan 0.0100 0.0001
## 340 0.5700 nan 0.0100 0.0001
## 360 0.5577 nan 0.0100 0.0001
## 380 0.5463 nan 0.0100 -0.0000
## 400 0.5360 nan 0.0100 0.0000
## 420 0.5256 nan 0.0100 0.0001
## 440 0.5158 nan 0.0100 -0.0001
## 460 0.5056 nan 0.0100 -0.0000
## 480 0.4970 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0043
## 4 1.2854 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0041
## 6 1.2685 nan 0.0100 0.0039
## 7 1.2607 nan 0.0100 0.0031
## 8 1.2522 nan 0.0100 0.0038
## 9 1.2444 nan 0.0100 0.0035
## 10 1.2366 nan 0.0100 0.0035
## 20 1.1656 nan 0.0100 0.0030
## 40 1.0540 nan 0.0100 0.0020
## 60 0.9689 nan 0.0100 0.0018
## 80 0.9006 nan 0.0100 0.0011
## 100 0.8465 nan 0.0100 0.0010
## 120 0.8023 nan 0.0100 0.0006
## 140 0.7652 nan 0.0100 0.0004
## 160 0.7352 nan 0.0100 0.0002
## 180 0.7090 nan 0.0100 0.0003
## 200 0.6860 nan 0.0100 0.0002
## 220 0.6670 nan 0.0100 0.0001
## 240 0.6488 nan 0.0100 0.0001
## 260 0.6323 nan 0.0100 0.0001
## 280 0.6161 nan 0.0100 0.0000
## 300 0.6001 nan 0.0100 0.0001
## 320 0.5866 nan 0.0100 -0.0000
## 340 0.5739 nan 0.0100 -0.0000
## 360 0.5625 nan 0.0100 -0.0001
## 380 0.5516 nan 0.0100 -0.0001
## 400 0.5412 nan 0.0100 0.0001
## 420 0.5308 nan 0.0100 -0.0001
## 440 0.5213 nan 0.0100 0.0000
## 460 0.5118 nan 0.0100 -0.0001
## 480 0.5036 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0041
## 3 1.2940 nan 0.0100 0.0041
## 4 1.2851 nan 0.0100 0.0040
## 5 1.2767 nan 0.0100 0.0036
## 6 1.2682 nan 0.0100 0.0040
## 7 1.2596 nan 0.0100 0.0040
## 8 1.2521 nan 0.0100 0.0039
## 9 1.2444 nan 0.0100 0.0038
## 10 1.2370 nan 0.0100 0.0030
## 20 1.1678 nan 0.0100 0.0029
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9677 nan 0.0100 0.0017
## 80 0.9018 nan 0.0100 0.0012
## 100 0.8488 nan 0.0100 0.0009
## 120 0.8071 nan 0.0100 0.0006
## 140 0.7717 nan 0.0100 0.0006
## 160 0.7413 nan 0.0100 0.0006
## 180 0.7154 nan 0.0100 0.0001
## 200 0.6937 nan 0.0100 0.0001
## 220 0.6737 nan 0.0100 0.0002
## 240 0.6555 nan 0.0100 0.0002
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## 280 0.6226 nan 0.0100 0.0001
## 300 0.6083 nan 0.0100 0.0000
## 320 0.5957 nan 0.0100 -0.0000
## 340 0.5846 nan 0.0100 0.0001
## 360 0.5731 nan 0.0100 -0.0002
## 380 0.5616 nan 0.0100 0.0000
## 400 0.5509 nan 0.0100 -0.0001
## 420 0.5404 nan 0.0100 -0.0001
## 440 0.5306 nan 0.0100 -0.0000
## 460 0.5213 nan 0.0100 -0.0001
## 480 0.5127 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0046
## 2 1.3006 nan 0.0100 0.0045
## 3 1.2916 nan 0.0100 0.0039
## 4 1.2823 nan 0.0100 0.0046
## 5 1.2732 nan 0.0100 0.0038
## 6 1.2646 nan 0.0100 0.0036
## 7 1.2564 nan 0.0100 0.0030
## 8 1.2483 nan 0.0100 0.0036
## 9 1.2405 nan 0.0100 0.0035
## 10 1.2323 nan 0.0100 0.0039
## 20 1.1577 nan 0.0100 0.0031
## 40 1.0383 nan 0.0100 0.0021
## 60 0.9479 nan 0.0100 0.0018
## 80 0.8786 nan 0.0100 0.0013
## 100 0.8227 nan 0.0100 0.0010
## 120 0.7755 nan 0.0100 0.0007
## 140 0.7375 nan 0.0100 0.0005
## 160 0.7052 nan 0.0100 0.0005
## 180 0.6785 nan 0.0100 0.0002
## 200 0.6540 nan 0.0100 0.0001
## 220 0.6311 nan 0.0100 0.0003
## 240 0.6120 nan 0.0100 -0.0001
## 260 0.5934 nan 0.0100 0.0001
## 280 0.5758 nan 0.0100 0.0002
## 300 0.5607 nan 0.0100 0.0000
## 320 0.5462 nan 0.0100 -0.0002
## 340 0.5328 nan 0.0100 0.0000
## 360 0.5196 nan 0.0100 -0.0000
## 380 0.5070 nan 0.0100 -0.0001
## 400 0.4945 nan 0.0100 -0.0001
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## 460 0.4642 nan 0.0100 0.0001
## 480 0.4544 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2926 nan 0.0100 0.0039
## 4 1.2834 nan 0.0100 0.0042
## 5 1.2748 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0036
## 7 1.2570 nan 0.0100 0.0041
## 8 1.2488 nan 0.0100 0.0037
## 9 1.2406 nan 0.0100 0.0035
## 10 1.2331 nan 0.0100 0.0036
## 20 1.1583 nan 0.0100 0.0033
## 40 1.0416 nan 0.0100 0.0022
## 60 0.9517 nan 0.0100 0.0016
## 80 0.8816 nan 0.0100 0.0011
## 100 0.8247 nan 0.0100 0.0009
## 120 0.7781 nan 0.0100 0.0006
## 140 0.7405 nan 0.0100 0.0003
## 160 0.7095 nan 0.0100 0.0004
## 180 0.6818 nan 0.0100 0.0002
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## 220 0.6350 nan 0.0100 0.0002
## 240 0.6173 nan 0.0100 0.0000
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## 280 0.5819 nan 0.0100 0.0000
## 300 0.5666 nan 0.0100 0.0002
## 320 0.5534 nan 0.0100 0.0000
## 340 0.5402 nan 0.0100 -0.0000
## 360 0.5269 nan 0.0100 -0.0001
## 380 0.5148 nan 0.0100 -0.0001
## 400 0.5034 nan 0.0100 0.0000
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## 480 0.4614 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0044
## 2 1.3025 nan 0.0100 0.0042
## 3 1.2938 nan 0.0100 0.0041
## 4 1.2850 nan 0.0100 0.0037
## 5 1.2767 nan 0.0100 0.0041
## 6 1.2681 nan 0.0100 0.0035
## 7 1.2594 nan 0.0100 0.0036
## 8 1.2513 nan 0.0100 0.0036
## 9 1.2432 nan 0.0100 0.0039
## 10 1.2352 nan 0.0100 0.0036
## 20 1.1644 nan 0.0100 0.0029
## 40 1.0489 nan 0.0100 0.0018
## 60 0.9600 nan 0.0100 0.0016
## 80 0.8907 nan 0.0100 0.0012
## 100 0.8356 nan 0.0100 0.0006
## 120 0.7890 nan 0.0100 0.0008
## 140 0.7520 nan 0.0100 0.0005
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## 180 0.6915 nan 0.0100 0.0002
## 200 0.6673 nan 0.0100 0.0001
## 220 0.6444 nan 0.0100 -0.0001
## 240 0.6258 nan 0.0100 -0.0001
## 260 0.6075 nan 0.0100 0.0002
## 280 0.5922 nan 0.0100 0.0000
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## 320 0.5653 nan 0.0100 -0.0000
## 340 0.5515 nan 0.0100 0.0002
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## 380 0.5274 nan 0.0100 0.0001
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## 460 0.4842 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2337 nan 0.1000 0.0392
## 2 1.1690 nan 0.1000 0.0307
## 3 1.1077 nan 0.1000 0.0251
## 4 1.0593 nan 0.1000 0.0213
## 5 1.0165 nan 0.1000 0.0154
## 6 0.9818 nan 0.1000 0.0132
## 7 0.9456 nan 0.1000 0.0128
## 8 0.9192 nan 0.1000 0.0114
## 9 0.8905 nan 0.1000 0.0111
## 10 0.8703 nan 0.1000 0.0069
## 20 0.7197 nan 0.1000 0.0012
## 40 0.5841 nan 0.1000 0.0009
## 60 0.5108 nan 0.1000 -0.0005
## 80 0.4516 nan 0.1000 -0.0006
## 100 0.4028 nan 0.1000 -0.0003
## 120 0.3538 nan 0.1000 -0.0019
## 140 0.3151 nan 0.1000 -0.0013
## 160 0.2817 nan 0.1000 -0.0009
## 180 0.2559 nan 0.1000 -0.0003
## 200 0.2306 nan 0.1000 -0.0002
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## 240 0.1916 nan 0.1000 -0.0006
## 260 0.1767 nan 0.1000 -0.0002
## 280 0.1600 nan 0.1000 -0.0002
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## 340 0.1204 nan 0.1000 0.0000
## 360 0.1100 nan 0.1000 -0.0002
## 380 0.1006 nan 0.1000 -0.0001
## 400 0.0931 nan 0.1000 -0.0002
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## 480 0.0679 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2379 nan 0.1000 0.0399
## 2 1.1701 nan 0.1000 0.0307
## 3 1.1100 nan 0.1000 0.0240
## 4 1.0603 nan 0.1000 0.0212
## 5 1.0161 nan 0.1000 0.0195
## 6 0.9818 nan 0.1000 0.0133
## 7 0.9526 nan 0.1000 0.0112
## 8 0.9219 nan 0.1000 0.0143
## 9 0.8986 nan 0.1000 0.0098
## 10 0.8720 nan 0.1000 0.0099
## 20 0.7206 nan 0.1000 -0.0001
## 40 0.5895 nan 0.1000 -0.0015
## 60 0.5182 nan 0.1000 -0.0005
## 80 0.4565 nan 0.1000 -0.0008
## 100 0.4004 nan 0.1000 -0.0009
## 120 0.3546 nan 0.1000 -0.0009
## 140 0.3199 nan 0.1000 -0.0010
## 160 0.2923 nan 0.1000 -0.0009
## 180 0.2661 nan 0.1000 -0.0004
## 200 0.2416 nan 0.1000 -0.0005
## 220 0.2242 nan 0.1000 -0.0001
## 240 0.2037 nan 0.1000 -0.0007
## 260 0.1860 nan 0.1000 -0.0003
## 280 0.1696 nan 0.1000 -0.0003
## 300 0.1553 nan 0.1000 -0.0006
## 320 0.1439 nan 0.1000 -0.0009
## 340 0.1333 nan 0.1000 -0.0004
## 360 0.1228 nan 0.1000 -0.0006
## 380 0.1131 nan 0.1000 -0.0004
## 400 0.1047 nan 0.1000 -0.0004
## 420 0.0977 nan 0.1000 -0.0003
## 440 0.0894 nan 0.1000 -0.0002
## 460 0.0831 nan 0.1000 -0.0003
## 480 0.0771 nan 0.1000 -0.0002
## 500 0.0711 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2367 nan 0.1000 0.0380
## 2 1.1688 nan 0.1000 0.0303
## 3 1.1094 nan 0.1000 0.0282
## 4 1.0593 nan 0.1000 0.0214
## 5 1.0180 nan 0.1000 0.0177
## 6 0.9795 nan 0.1000 0.0157
## 7 0.9419 nan 0.1000 0.0144
## 8 0.9133 nan 0.1000 0.0119
## 9 0.8899 nan 0.1000 0.0085
## 10 0.8686 nan 0.1000 0.0077
## 20 0.7267 nan 0.1000 0.0023
## 40 0.5945 nan 0.1000 -0.0006
## 60 0.5221 nan 0.1000 -0.0002
## 80 0.4607 nan 0.1000 -0.0005
## 100 0.4196 nan 0.1000 -0.0007
## 120 0.3841 nan 0.1000 -0.0006
## 140 0.3422 nan 0.1000 -0.0000
## 160 0.3064 nan 0.1000 -0.0010
## 180 0.2829 nan 0.1000 -0.0010
## 200 0.2595 nan 0.1000 -0.0001
## 220 0.2365 nan 0.1000 -0.0005
## 240 0.2162 nan 0.1000 -0.0008
## 260 0.1997 nan 0.1000 -0.0001
## 280 0.1832 nan 0.1000 -0.0008
## 300 0.1677 nan 0.1000 -0.0007
## 320 0.1550 nan 0.1000 -0.0008
## 340 0.1406 nan 0.1000 -0.0002
## 360 0.1299 nan 0.1000 -0.0007
## 380 0.1199 nan 0.1000 -0.0004
## 400 0.1092 nan 0.1000 -0.0001
## 420 0.1011 nan 0.1000 -0.0006
## 440 0.0939 nan 0.1000 -0.0003
## 460 0.0864 nan 0.1000 -0.0002
## 480 0.0813 nan 0.1000 -0.0005
## 500 0.0752 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0361
## 2 1.1587 nan 0.1000 0.0378
## 3 1.0954 nan 0.1000 0.0289
## 4 1.0418 nan 0.1000 0.0207
## 5 0.9956 nan 0.1000 0.0201
## 6 0.9566 nan 0.1000 0.0149
## 7 0.9245 nan 0.1000 0.0118
## 8 0.8899 nan 0.1000 0.0154
## 9 0.8620 nan 0.1000 0.0114
## 10 0.8359 nan 0.1000 0.0084
## 20 0.6763 nan 0.1000 0.0025
## 40 0.5433 nan 0.1000 0.0005
## 60 0.4520 nan 0.1000 -0.0005
## 80 0.3856 nan 0.1000 -0.0003
## 100 0.3382 nan 0.1000 -0.0005
## 120 0.2954 nan 0.1000 -0.0009
## 140 0.2597 nan 0.1000 -0.0003
## 160 0.2292 nan 0.1000 -0.0008
## 180 0.2055 nan 0.1000 0.0001
## 200 0.1797 nan 0.1000 0.0001
## 220 0.1583 nan 0.1000 -0.0003
## 240 0.1396 nan 0.1000 -0.0006
## 260 0.1234 nan 0.1000 -0.0002
## 280 0.1104 nan 0.1000 -0.0003
## 300 0.0993 nan 0.1000 -0.0002
## 320 0.0895 nan 0.1000 -0.0003
## 340 0.0807 nan 0.1000 -0.0001
## 360 0.0728 nan 0.1000 -0.0001
## 380 0.0661 nan 0.1000 -0.0002
## 400 0.0597 nan 0.1000 -0.0001
## 420 0.0540 nan 0.1000 -0.0003
## 440 0.0487 nan 0.1000 -0.0001
## 460 0.0447 nan 0.1000 -0.0000
## 480 0.0409 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0403
## 2 1.1645 nan 0.1000 0.0335
## 3 1.1017 nan 0.1000 0.0279
## 4 1.0505 nan 0.1000 0.0205
## 5 1.0067 nan 0.1000 0.0219
## 6 0.9663 nan 0.1000 0.0168
## 7 0.9313 nan 0.1000 0.0142
## 8 0.9046 nan 0.1000 0.0091
## 9 0.8769 nan 0.1000 0.0106
## 10 0.8496 nan 0.1000 0.0113
## 20 0.6862 nan 0.1000 0.0002
## 40 0.5537 nan 0.1000 -0.0035
## 60 0.4607 nan 0.1000 -0.0002
## 80 0.3971 nan 0.1000 -0.0018
## 100 0.3405 nan 0.1000 -0.0013
## 120 0.2992 nan 0.1000 -0.0011
## 140 0.2610 nan 0.1000 -0.0015
## 160 0.2296 nan 0.1000 -0.0008
## 180 0.2001 nan 0.1000 -0.0002
## 200 0.1786 nan 0.1000 -0.0006
## 220 0.1568 nan 0.1000 -0.0003
## 240 0.1377 nan 0.1000 -0.0003
## 260 0.1237 nan 0.1000 -0.0003
## 280 0.1097 nan 0.1000 -0.0003
## 300 0.0991 nan 0.1000 -0.0003
## 320 0.0905 nan 0.1000 -0.0001
## 340 0.0807 nan 0.1000 -0.0001
## 360 0.0732 nan 0.1000 -0.0003
## 380 0.0659 nan 0.1000 -0.0002
## 400 0.0596 nan 0.1000 -0.0005
## 420 0.0544 nan 0.1000 -0.0000
## 440 0.0497 nan 0.1000 -0.0002
## 460 0.0450 nan 0.1000 -0.0001
## 480 0.0409 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0391
## 2 1.1585 nan 0.1000 0.0318
## 3 1.0962 nan 0.1000 0.0278
## 4 1.0445 nan 0.1000 0.0229
## 5 0.9953 nan 0.1000 0.0214
## 6 0.9532 nan 0.1000 0.0164
## 7 0.9210 nan 0.1000 0.0136
## 8 0.8922 nan 0.1000 0.0122
## 9 0.8656 nan 0.1000 0.0096
## 10 0.8408 nan 0.1000 0.0088
## 20 0.6867 nan 0.1000 0.0020
## 40 0.5505 nan 0.1000 0.0005
## 60 0.4689 nan 0.1000 -0.0001
## 80 0.4071 nan 0.1000 -0.0012
## 100 0.3501 nan 0.1000 -0.0006
## 120 0.3076 nan 0.1000 -0.0014
## 140 0.2699 nan 0.1000 -0.0009
## 160 0.2396 nan 0.1000 -0.0009
## 180 0.2125 nan 0.1000 -0.0002
## 200 0.1897 nan 0.1000 -0.0006
## 220 0.1689 nan 0.1000 -0.0005
## 240 0.1506 nan 0.1000 -0.0009
## 260 0.1347 nan 0.1000 -0.0005
## 280 0.1204 nan 0.1000 -0.0002
## 300 0.1082 nan 0.1000 -0.0005
## 320 0.0982 nan 0.1000 -0.0006
## 340 0.0886 nan 0.1000 -0.0003
## 360 0.0808 nan 0.1000 -0.0003
## 380 0.0731 nan 0.1000 -0.0002
## 400 0.0671 nan 0.1000 -0.0002
## 420 0.0615 nan 0.1000 -0.0003
## 440 0.0556 nan 0.1000 -0.0001
## 460 0.0506 nan 0.1000 -0.0001
## 480 0.0458 nan 0.1000 -0.0001
## 500 0.0416 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2278 nan 0.1000 0.0408
## 2 1.1612 nan 0.1000 0.0271
## 3 1.0993 nan 0.1000 0.0240
## 4 1.0385 nan 0.1000 0.0243
## 5 0.9870 nan 0.1000 0.0236
## 6 0.9473 nan 0.1000 0.0167
## 7 0.9082 nan 0.1000 0.0162
## 8 0.8742 nan 0.1000 0.0126
## 9 0.8434 nan 0.1000 0.0110
## 10 0.8180 nan 0.1000 0.0093
## 20 0.6514 nan 0.1000 0.0033
## 40 0.4848 nan 0.1000 0.0008
## 60 0.3953 nan 0.1000 -0.0009
## 80 0.3238 nan 0.1000 -0.0011
## 100 0.2703 nan 0.1000 -0.0009
## 120 0.2294 nan 0.1000 -0.0005
## 140 0.1966 nan 0.1000 -0.0005
## 160 0.1667 nan 0.1000 -0.0005
## 180 0.1441 nan 0.1000 -0.0001
## 200 0.1249 nan 0.1000 -0.0006
## 220 0.1108 nan 0.1000 -0.0004
## 240 0.0950 nan 0.1000 -0.0003
## 260 0.0824 nan 0.1000 -0.0003
## 280 0.0730 nan 0.1000 -0.0000
## 300 0.0657 nan 0.1000 -0.0001
## 320 0.0586 nan 0.1000 -0.0002
## 340 0.0518 nan 0.1000 0.0001
## 360 0.0466 nan 0.1000 -0.0001
## 380 0.0413 nan 0.1000 -0.0002
## 400 0.0365 nan 0.1000 -0.0001
## 420 0.0322 nan 0.1000 -0.0001
## 440 0.0290 nan 0.1000 -0.0001
## 460 0.0260 nan 0.1000 -0.0002
## 480 0.0230 nan 0.1000 -0.0000
## 500 0.0205 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0369
## 2 1.1489 nan 0.1000 0.0330
## 3 1.0807 nan 0.1000 0.0310
## 4 1.0243 nan 0.1000 0.0238
## 5 0.9795 nan 0.1000 0.0180
## 6 0.9342 nan 0.1000 0.0173
## 7 0.8992 nan 0.1000 0.0145
## 8 0.8627 nan 0.1000 0.0149
## 9 0.8343 nan 0.1000 0.0118
## 10 0.8089 nan 0.1000 0.0076
## 20 0.6499 nan 0.1000 0.0012
## 40 0.5033 nan 0.1000 -0.0004
## 60 0.4034 nan 0.1000 -0.0024
## 80 0.3358 nan 0.1000 0.0006
## 100 0.2801 nan 0.1000 -0.0015
## 120 0.2370 nan 0.1000 -0.0011
## 140 0.2026 nan 0.1000 -0.0010
## 160 0.1730 nan 0.1000 -0.0002
## 180 0.1488 nan 0.1000 -0.0008
## 200 0.1306 nan 0.1000 -0.0003
## 220 0.1152 nan 0.1000 -0.0003
## 240 0.1017 nan 0.1000 0.0000
## 260 0.0878 nan 0.1000 -0.0005
## 280 0.0773 nan 0.1000 -0.0002
## 300 0.0679 nan 0.1000 -0.0001
## 320 0.0607 nan 0.1000 -0.0001
## 340 0.0542 nan 0.1000 -0.0003
## 360 0.0482 nan 0.1000 -0.0002
## 380 0.0426 nan 0.1000 -0.0001
## 400 0.0375 nan 0.1000 -0.0000
## 420 0.0334 nan 0.1000 -0.0002
## 440 0.0298 nan 0.1000 -0.0002
## 460 0.0266 nan 0.1000 -0.0001
## 480 0.0234 nan 0.1000 -0.0001
## 500 0.0209 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2307 nan 0.1000 0.0413
## 2 1.1592 nan 0.1000 0.0298
## 3 1.1007 nan 0.1000 0.0263
## 4 1.0469 nan 0.1000 0.0231
## 5 0.9975 nan 0.1000 0.0208
## 6 0.9555 nan 0.1000 0.0167
## 7 0.9192 nan 0.1000 0.0128
## 8 0.8909 nan 0.1000 0.0096
## 9 0.8631 nan 0.1000 0.0102
## 10 0.8363 nan 0.1000 0.0095
## 20 0.6783 nan 0.1000 0.0007
## 40 0.5227 nan 0.1000 0.0004
## 60 0.4303 nan 0.1000 -0.0012
## 80 0.3577 nan 0.1000 -0.0006
## 100 0.3042 nan 0.1000 -0.0005
## 120 0.2632 nan 0.1000 -0.0007
## 140 0.2217 nan 0.1000 -0.0010
## 160 0.1936 nan 0.1000 -0.0006
## 180 0.1672 nan 0.1000 -0.0005
## 200 0.1459 nan 0.1000 -0.0006
## 220 0.1281 nan 0.1000 -0.0004
## 240 0.1137 nan 0.1000 -0.0004
## 260 0.1015 nan 0.1000 -0.0004
## 280 0.0902 nan 0.1000 -0.0002
## 300 0.0791 nan 0.1000 -0.0004
## 320 0.0696 nan 0.1000 -0.0003
## 340 0.0616 nan 0.1000 -0.0002
## 360 0.0554 nan 0.1000 -0.0003
## 380 0.0497 nan 0.1000 -0.0001
## 400 0.0447 nan 0.1000 -0.0002
## 420 0.0399 nan 0.1000 -0.0002
## 440 0.0360 nan 0.1000 -0.0001
## 460 0.0319 nan 0.1000 -0.0001
## 480 0.0285 nan 0.1000 -0.0002
## 500 0.0253 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2878 nan 0.0010 0.0003
## 60 1.2721 nan 0.0010 0.0004
## 80 1.2571 nan 0.0010 0.0003
## 100 1.2425 nan 0.0010 0.0004
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2147 nan 0.0010 0.0003
## 160 1.2013 nan 0.0010 0.0003
## 180 1.1886 nan 0.0010 0.0003
## 200 1.1762 nan 0.0010 0.0003
## 220 1.1639 nan 0.0010 0.0003
## 240 1.1521 nan 0.0010 0.0002
## 260 1.1407 nan 0.0010 0.0003
## 280 1.1297 nan 0.0010 0.0002
## 300 1.1189 nan 0.0010 0.0002
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0986 nan 0.0010 0.0002
## 360 1.0889 nan 0.0010 0.0002
## 380 1.0794 nan 0.0010 0.0002
## 400 1.0700 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0521 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0348 nan 0.0010 0.0002
## 500 1.0266 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0003
## 60 1.2722 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2155 nan 0.0010 0.0003
## 160 1.2025 nan 0.0010 0.0003
## 180 1.1896 nan 0.0010 0.0003
## 200 1.1772 nan 0.0010 0.0003
## 220 1.1652 nan 0.0010 0.0002
## 240 1.1535 nan 0.0010 0.0002
## 260 1.1423 nan 0.0010 0.0002
## 280 1.1313 nan 0.0010 0.0002
## 300 1.1207 nan 0.0010 0.0002
## 320 1.1103 nan 0.0010 0.0002
## 340 1.1003 nan 0.0010 0.0002
## 360 1.0903 nan 0.0010 0.0002
## 380 1.0809 nan 0.0010 0.0002
## 400 1.0715 nan 0.0010 0.0002
## 420 1.0625 nan 0.0010 0.0002
## 440 1.0537 nan 0.0010 0.0002
## 460 1.0448 nan 0.0010 0.0002
## 480 1.0366 nan 0.0010 0.0002
## 500 1.0286 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2727 nan 0.0010 0.0003
## 80 1.2577 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2154 nan 0.0010 0.0003
## 160 1.2022 nan 0.0010 0.0003
## 180 1.1894 nan 0.0010 0.0003
## 200 1.1771 nan 0.0010 0.0003
## 220 1.1652 nan 0.0010 0.0003
## 240 1.1537 nan 0.0010 0.0003
## 260 1.1425 nan 0.0010 0.0002
## 280 1.1316 nan 0.0010 0.0002
## 300 1.1210 nan 0.0010 0.0003
## 320 1.1107 nan 0.0010 0.0002
## 340 1.1009 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0723 nan 0.0010 0.0002
## 420 1.0631 nan 0.0010 0.0002
## 440 1.0542 nan 0.0010 0.0002
## 460 1.0456 nan 0.0010 0.0002
## 480 1.0373 nan 0.0010 0.0002
## 500 1.0291 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0003
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0003
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0003
## 100 1.2377 nan 0.0010 0.0003
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2083 nan 0.0010 0.0003
## 160 1.1941 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1668 nan 0.0010 0.0003
## 220 1.1540 nan 0.0010 0.0003
## 240 1.1417 nan 0.0010 0.0002
## 260 1.1296 nan 0.0010 0.0002
## 280 1.1179 nan 0.0010 0.0003
## 300 1.1064 nan 0.0010 0.0003
## 320 1.0953 nan 0.0010 0.0003
## 340 1.0844 nan 0.0010 0.0002
## 360 1.0743 nan 0.0010 0.0002
## 380 1.0641 nan 0.0010 0.0002
## 400 1.0541 nan 0.0010 0.0002
## 420 1.0445 nan 0.0010 0.0002
## 440 1.0350 nan 0.0010 0.0002
## 460 1.0261 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0084 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2534 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0004
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0004
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1808 nan 0.0010 0.0003
## 200 1.1676 nan 0.0010 0.0003
## 220 1.1547 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0002
## 260 1.1304 nan 0.0010 0.0003
## 280 1.1184 nan 0.0010 0.0002
## 300 1.1072 nan 0.0010 0.0002
## 320 1.0961 nan 0.0010 0.0003
## 340 1.0852 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0650 nan 0.0010 0.0002
## 400 1.0551 nan 0.0010 0.0002
## 420 1.0455 nan 0.0010 0.0002
## 440 1.0361 nan 0.0010 0.0002
## 460 1.0268 nan 0.0010 0.0002
## 480 1.0180 nan 0.0010 0.0002
## 500 1.0094 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0003
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0003
## 60 1.2691 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0003
## 120 1.2226 nan 0.0010 0.0003
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
## 200 1.1673 nan 0.0010 0.0003
## 220 1.1546 nan 0.0010 0.0003
## 240 1.1422 nan 0.0010 0.0003
## 260 1.1300 nan 0.0010 0.0003
## 280 1.1184 nan 0.0010 0.0002
## 300 1.1071 nan 0.0010 0.0003
## 320 1.0960 nan 0.0010 0.0002
## 340 1.0856 nan 0.0010 0.0002
## 360 1.0753 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0557 nan 0.0010 0.0002
## 420 1.0463 nan 0.0010 0.0002
## 440 1.0370 nan 0.0010 0.0002
## 460 1.0277 nan 0.0010 0.0002
## 480 1.0189 nan 0.0010 0.0002
## 500 1.0103 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0005
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2659 nan 0.0010 0.0004
## 80 1.2488 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2163 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
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## 260 1.1193 nan 0.0010 0.0003
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## 300 1.0952 nan 0.0010 0.0002
## 320 1.0836 nan 0.0010 0.0003
## 340 1.0726 nan 0.0010 0.0003
## 360 1.0616 nan 0.0010 0.0002
## 380 1.0509 nan 0.0010 0.0002
## 400 1.0405 nan 0.0010 0.0002
## 420 1.0302 nan 0.0010 0.0002
## 440 1.0203 nan 0.0010 0.0002
## 460 1.0106 nan 0.0010 0.0002
## 480 1.0013 nan 0.0010 0.0002
## 500 0.9924 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3113 nan 0.0010 0.0005
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2499 nan 0.0010 0.0004
## 100 1.2338 nan 0.0010 0.0004
## 120 1.2182 nan 0.0010 0.0004
## 140 1.2029 nan 0.0010 0.0003
## 160 1.1883 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0003
## 200 1.1599 nan 0.0010 0.0003
## 220 1.1463 nan 0.0010 0.0003
## 240 1.1334 nan 0.0010 0.0003
## 260 1.1209 nan 0.0010 0.0003
## 280 1.1083 nan 0.0010 0.0003
## 300 1.0966 nan 0.0010 0.0003
## 320 1.0850 nan 0.0010 0.0002
## 340 1.0738 nan 0.0010 0.0002
## 360 1.0631 nan 0.0010 0.0002
## 380 1.0525 nan 0.0010 0.0002
## 400 1.0420 nan 0.0010 0.0002
## 420 1.0320 nan 0.0010 0.0002
## 440 1.0225 nan 0.0010 0.0002
## 460 1.0129 nan 0.0010 0.0002
## 480 1.0035 nan 0.0010 0.0002
## 500 0.9946 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0005
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0003
## 80 1.2505 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2196 nan 0.0010 0.0004
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1896 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1617 nan 0.0010 0.0003
## 220 1.1485 nan 0.0010 0.0003
## 240 1.1356 nan 0.0010 0.0003
## 260 1.1233 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0996 nan 0.0010 0.0002
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0771 nan 0.0010 0.0002
## 360 1.0664 nan 0.0010 0.0002
## 380 1.0560 nan 0.0010 0.0002
## 400 1.0459 nan 0.0010 0.0002
## 420 1.0362 nan 0.0010 0.0002
## 440 1.0265 nan 0.0010 0.0002
## 460 1.0173 nan 0.0010 0.0002
## 480 1.0080 nan 0.0010 0.0002
## 500 0.9989 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2957 nan 0.0100 0.0036
## 4 1.2873 nan 0.0100 0.0036
## 5 1.2797 nan 0.0100 0.0034
## 6 1.2711 nan 0.0100 0.0036
## 7 1.2642 nan 0.0100 0.0031
## 8 1.2569 nan 0.0100 0.0032
## 9 1.2500 nan 0.0100 0.0030
## 10 1.2432 nan 0.0100 0.0032
## 20 1.1746 nan 0.0100 0.0029
## 40 1.0700 nan 0.0100 0.0018
## 60 0.9879 nan 0.0100 0.0014
## 80 0.9245 nan 0.0100 0.0011
## 100 0.8739 nan 0.0100 0.0009
## 120 0.8317 nan 0.0100 0.0004
## 140 0.7969 nan 0.0100 0.0004
## 160 0.7670 nan 0.0100 0.0003
## 180 0.7420 nan 0.0100 0.0001
## 200 0.7194 nan 0.0100 0.0002
## 220 0.7011 nan 0.0100 0.0002
## 240 0.6837 nan 0.0100 0.0000
## 260 0.6692 nan 0.0100 0.0000
## 280 0.6549 nan 0.0100 0.0001
## 300 0.6419 nan 0.0100 -0.0000
## 320 0.6292 nan 0.0100 0.0002
## 340 0.6174 nan 0.0100 0.0001
## 360 0.6058 nan 0.0100 0.0000
## 380 0.5948 nan 0.0100 0.0001
## 400 0.5846 nan 0.0100 0.0000
## 420 0.5760 nan 0.0100 -0.0001
## 440 0.5666 nan 0.0100 -0.0000
## 460 0.5581 nan 0.0100 -0.0001
## 480 0.5488 nan 0.0100 0.0000
## 500 0.5404 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0036
## 2 1.3041 nan 0.0100 0.0035
## 3 1.2960 nan 0.0100 0.0039
## 4 1.2882 nan 0.0100 0.0034
## 5 1.2802 nan 0.0100 0.0034
## 6 1.2725 nan 0.0100 0.0038
## 7 1.2651 nan 0.0100 0.0033
## 8 1.2573 nan 0.0100 0.0033
## 9 1.2496 nan 0.0100 0.0036
## 10 1.2429 nan 0.0100 0.0028
## 20 1.1758 nan 0.0100 0.0031
## 40 1.0696 nan 0.0100 0.0018
## 60 0.9907 nan 0.0100 0.0013
## 80 0.9244 nan 0.0100 0.0012
## 100 0.8730 nan 0.0100 0.0007
## 120 0.8305 nan 0.0100 0.0006
## 140 0.7953 nan 0.0100 0.0007
## 160 0.7667 nan 0.0100 0.0005
## 180 0.7422 nan 0.0100 0.0003
## 200 0.7201 nan 0.0100 0.0004
## 220 0.7015 nan 0.0100 0.0003
## 240 0.6855 nan 0.0100 0.0000
## 260 0.6704 nan 0.0100 -0.0001
## 280 0.6568 nan 0.0100 -0.0000
## 300 0.6437 nan 0.0100 0.0001
## 320 0.6320 nan 0.0100 0.0001
## 340 0.6216 nan 0.0100 0.0001
## 360 0.6109 nan 0.0100 -0.0000
## 380 0.6017 nan 0.0100 0.0001
## 400 0.5923 nan 0.0100 -0.0001
## 420 0.5839 nan 0.0100 -0.0001
## 440 0.5750 nan 0.0100 -0.0001
## 460 0.5672 nan 0.0100 0.0000
## 480 0.5581 nan 0.0100 -0.0001
## 500 0.5502 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3039 nan 0.0100 0.0038
## 3 1.2961 nan 0.0100 0.0036
## 4 1.2884 nan 0.0100 0.0036
## 5 1.2809 nan 0.0100 0.0035
## 6 1.2732 nan 0.0100 0.0031
## 7 1.2663 nan 0.0100 0.0032
## 8 1.2595 nan 0.0100 0.0031
## 9 1.2528 nan 0.0100 0.0033
## 10 1.2455 nan 0.0100 0.0037
## 20 1.1790 nan 0.0100 0.0029
## 40 1.0755 nan 0.0100 0.0017
## 60 0.9948 nan 0.0100 0.0011
## 80 0.9301 nan 0.0100 0.0011
## 100 0.8780 nan 0.0100 0.0009
## 120 0.8360 nan 0.0100 0.0005
## 140 0.8005 nan 0.0100 0.0005
## 160 0.7721 nan 0.0100 0.0004
## 180 0.7466 nan 0.0100 0.0003
## 200 0.7267 nan 0.0100 0.0002
## 220 0.7078 nan 0.0100 -0.0001
## 240 0.6912 nan 0.0100 0.0002
## 260 0.6771 nan 0.0100 0.0002
## 280 0.6632 nan 0.0100 0.0000
## 300 0.6504 nan 0.0100 -0.0000
## 320 0.6392 nan 0.0100 0.0001
## 340 0.6278 nan 0.0100 0.0001
## 360 0.6174 nan 0.0100 -0.0001
## 380 0.6077 nan 0.0100 0.0001
## 400 0.5988 nan 0.0100 -0.0000
## 420 0.5900 nan 0.0100 -0.0001
## 440 0.5817 nan 0.0100 -0.0000
## 460 0.5738 nan 0.0100 0.0000
## 480 0.5654 nan 0.0100 0.0000
## 500 0.5580 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2939 nan 0.0100 0.0039
## 4 1.2861 nan 0.0100 0.0035
## 5 1.2772 nan 0.0100 0.0037
## 6 1.2689 nan 0.0100 0.0034
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2522 nan 0.0100 0.0035
## 9 1.2448 nan 0.0100 0.0033
## 10 1.2364 nan 0.0100 0.0038
## 20 1.1657 nan 0.0100 0.0032
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9672 nan 0.0100 0.0016
## 80 0.8995 nan 0.0100 0.0011
## 100 0.8456 nan 0.0100 0.0009
## 120 0.8015 nan 0.0100 0.0007
## 140 0.7648 nan 0.0100 0.0004
## 160 0.7347 nan 0.0100 0.0005
## 180 0.7072 nan 0.0100 0.0002
## 200 0.6846 nan 0.0100 0.0003
## 220 0.6625 nan 0.0100 0.0001
## 240 0.6442 nan 0.0100 0.0002
## 260 0.6268 nan 0.0100 0.0003
## 280 0.6105 nan 0.0100 0.0001
## 300 0.5960 nan 0.0100 0.0000
## 320 0.5822 nan 0.0100 -0.0001
## 340 0.5698 nan 0.0100 0.0000
## 360 0.5580 nan 0.0100 0.0001
## 380 0.5473 nan 0.0100 -0.0000
## 400 0.5369 nan 0.0100 0.0001
## 420 0.5251 nan 0.0100 0.0001
## 440 0.5150 nan 0.0100 -0.0001
## 460 0.5052 nan 0.0100 0.0001
## 480 0.4968 nan 0.0100 -0.0001
## 500 0.4880 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3030 nan 0.0100 0.0039
## 3 1.2941 nan 0.0100 0.0039
## 4 1.2850 nan 0.0100 0.0039
## 5 1.2769 nan 0.0100 0.0033
## 6 1.2681 nan 0.0100 0.0040
## 7 1.2596 nan 0.0100 0.0035
## 8 1.2510 nan 0.0100 0.0037
## 9 1.2434 nan 0.0100 0.0037
## 10 1.2348 nan 0.0100 0.0037
## 20 1.1642 nan 0.0100 0.0033
## 40 1.0529 nan 0.0100 0.0020
## 60 0.9687 nan 0.0100 0.0017
## 80 0.9037 nan 0.0100 0.0013
## 100 0.8500 nan 0.0100 0.0010
## 120 0.8067 nan 0.0100 0.0006
## 140 0.7692 nan 0.0100 0.0007
## 160 0.7382 nan 0.0100 0.0004
## 180 0.7125 nan 0.0100 0.0003
## 200 0.6904 nan 0.0100 0.0004
## 220 0.6696 nan 0.0100 0.0002
## 240 0.6511 nan 0.0100 0.0001
## 260 0.6341 nan 0.0100 0.0001
## 280 0.6189 nan 0.0100 0.0002
## 300 0.6054 nan 0.0100 0.0000
## 320 0.5918 nan 0.0100 -0.0001
## 340 0.5799 nan 0.0100 -0.0000
## 360 0.5676 nan 0.0100 0.0000
## 380 0.5557 nan 0.0100 -0.0000
## 400 0.5443 nan 0.0100 -0.0001
## 420 0.5339 nan 0.0100 -0.0001
## 440 0.5241 nan 0.0100 -0.0000
## 460 0.5133 nan 0.0100 0.0000
## 480 0.5039 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0038
## 2 1.3031 nan 0.0100 0.0037
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2770 nan 0.0100 0.0041
## 6 1.2691 nan 0.0100 0.0038
## 7 1.2604 nan 0.0100 0.0040
## 8 1.2526 nan 0.0100 0.0032
## 9 1.2441 nan 0.0100 0.0040
## 10 1.2363 nan 0.0100 0.0035
## 20 1.1679 nan 0.0100 0.0025
## 40 1.0532 nan 0.0100 0.0020
## 60 0.9679 nan 0.0100 0.0015
## 80 0.9025 nan 0.0100 0.0009
## 100 0.8498 nan 0.0100 0.0009
## 120 0.8069 nan 0.0100 0.0005
## 140 0.7717 nan 0.0100 0.0005
## 160 0.7430 nan 0.0100 0.0004
## 180 0.7168 nan 0.0100 0.0003
## 200 0.6952 nan 0.0100 0.0002
## 220 0.6745 nan 0.0100 0.0001
## 240 0.6575 nan 0.0100 0.0001
## 260 0.6413 nan 0.0100 0.0000
## 280 0.6270 nan 0.0100 -0.0001
## 300 0.6137 nan 0.0100 0.0001
## 320 0.6013 nan 0.0100 0.0001
## 340 0.5887 nan 0.0100 -0.0000
## 360 0.5771 nan 0.0100 0.0001
## 380 0.5658 nan 0.0100 0.0000
## 400 0.5554 nan 0.0100 -0.0001
## 420 0.5455 nan 0.0100 0.0000
## 440 0.5361 nan 0.0100 -0.0001
## 460 0.5264 nan 0.0100 -0.0000
## 480 0.5171 nan 0.0100 -0.0002
## 500 0.5088 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0045
## 2 1.3012 nan 0.0100 0.0046
## 3 1.2918 nan 0.0100 0.0045
## 4 1.2824 nan 0.0100 0.0044
## 5 1.2737 nan 0.0100 0.0040
## 6 1.2649 nan 0.0100 0.0040
## 7 1.2562 nan 0.0100 0.0038
## 8 1.2483 nan 0.0100 0.0039
## 9 1.2402 nan 0.0100 0.0031
## 10 1.2319 nan 0.0100 0.0038
## 20 1.1586 nan 0.0100 0.0027
## 40 1.0401 nan 0.0100 0.0022
## 60 0.9509 nan 0.0100 0.0014
## 80 0.8819 nan 0.0100 0.0013
## 100 0.8257 nan 0.0100 0.0010
## 120 0.7800 nan 0.0100 0.0009
## 140 0.7403 nan 0.0100 0.0003
## 160 0.7092 nan 0.0100 0.0003
## 180 0.6819 nan 0.0100 0.0003
## 200 0.6576 nan 0.0100 0.0001
## 220 0.6357 nan 0.0100 0.0003
## 240 0.6145 nan 0.0100 0.0003
## 260 0.5962 nan 0.0100 0.0001
## 280 0.5784 nan 0.0100 0.0002
## 300 0.5631 nan 0.0100 0.0000
## 320 0.5472 nan 0.0100 0.0002
## 340 0.5336 nan 0.0100 -0.0001
## 360 0.5191 nan 0.0100 0.0001
## 380 0.5068 nan 0.0100 -0.0001
## 400 0.4951 nan 0.0100 0.0001
## 420 0.4832 nan 0.0100 -0.0000
## 440 0.4723 nan 0.0100 0.0000
## 460 0.4624 nan 0.0100 -0.0000
## 480 0.4522 nan 0.0100 -0.0000
## 500 0.4426 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3023 nan 0.0100 0.0044
## 3 1.2933 nan 0.0100 0.0044
## 4 1.2839 nan 0.0100 0.0042
## 5 1.2739 nan 0.0100 0.0041
## 6 1.2658 nan 0.0100 0.0038
## 7 1.2578 nan 0.0100 0.0036
## 8 1.2503 nan 0.0100 0.0036
## 9 1.2418 nan 0.0100 0.0036
## 10 1.2327 nan 0.0100 0.0037
## 20 1.1570 nan 0.0100 0.0032
## 40 1.0400 nan 0.0100 0.0018
## 60 0.9505 nan 0.0100 0.0015
## 80 0.8804 nan 0.0100 0.0014
## 100 0.8243 nan 0.0100 0.0007
## 120 0.7783 nan 0.0100 0.0008
## 140 0.7421 nan 0.0100 0.0004
## 160 0.7109 nan 0.0100 0.0003
## 180 0.6831 nan 0.0100 0.0002
## 200 0.6578 nan 0.0100 0.0002
## 220 0.6376 nan 0.0100 -0.0000
## 240 0.6165 nan 0.0100 0.0001
## 260 0.5986 nan 0.0100 0.0003
## 280 0.5821 nan 0.0100 0.0003
## 300 0.5668 nan 0.0100 -0.0002
## 320 0.5529 nan 0.0100 0.0000
## 340 0.5393 nan 0.0100 -0.0001
## 360 0.5268 nan 0.0100 -0.0001
## 380 0.5143 nan 0.0100 0.0000
## 400 0.5030 nan 0.0100 -0.0003
## 420 0.4907 nan 0.0100 0.0001
## 440 0.4797 nan 0.0100 0.0001
## 460 0.4695 nan 0.0100 0.0000
## 480 0.4603 nan 0.0100 -0.0000
## 500 0.4516 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0045
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2935 nan 0.0100 0.0040
## 4 1.2853 nan 0.0100 0.0038
## 5 1.2764 nan 0.0100 0.0043
## 6 1.2673 nan 0.0100 0.0041
## 7 1.2592 nan 0.0100 0.0036
## 8 1.2503 nan 0.0100 0.0041
## 9 1.2424 nan 0.0100 0.0038
## 10 1.2337 nan 0.0100 0.0035
## 20 1.1603 nan 0.0100 0.0028
## 40 1.0434 nan 0.0100 0.0023
## 60 0.9569 nan 0.0100 0.0017
## 80 0.8892 nan 0.0100 0.0010
## 100 0.8339 nan 0.0100 0.0010
## 120 0.7884 nan 0.0100 0.0006
## 140 0.7507 nan 0.0100 0.0004
## 160 0.7187 nan 0.0100 0.0003
## 180 0.6906 nan 0.0100 0.0001
## 200 0.6669 nan 0.0100 0.0003
## 220 0.6468 nan 0.0100 0.0002
## 240 0.6268 nan 0.0100 0.0001
## 260 0.6091 nan 0.0100 0.0001
## 280 0.5935 nan 0.0100 0.0001
## 300 0.5786 nan 0.0100 -0.0001
## 320 0.5645 nan 0.0100 -0.0000
## 340 0.5512 nan 0.0100 0.0001
## 360 0.5393 nan 0.0100 -0.0001
## 380 0.5277 nan 0.0100 0.0000
## 400 0.5175 nan 0.0100 -0.0001
## 420 0.5071 nan 0.0100 -0.0003
## 440 0.4969 nan 0.0100 0.0002
## 460 0.4866 nan 0.0100 -0.0002
## 480 0.4765 nan 0.0100 -0.0000
## 500 0.4663 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0360
## 2 1.1745 nan 0.1000 0.0309
## 3 1.1186 nan 0.1000 0.0236
## 4 1.0661 nan 0.1000 0.0221
## 5 1.0243 nan 0.1000 0.0174
## 6 0.9889 nan 0.1000 0.0128
## 7 0.9577 nan 0.1000 0.0127
## 8 0.9232 nan 0.1000 0.0139
## 9 0.8942 nan 0.1000 0.0115
## 10 0.8699 nan 0.1000 0.0090
## 20 0.7230 nan 0.1000 0.0006
## 40 0.5935 nan 0.1000 0.0016
## 60 0.5053 nan 0.1000 0.0014
## 80 0.4435 nan 0.1000 0.0003
## 100 0.3895 nan 0.1000 -0.0009
## 120 0.3434 nan 0.1000 -0.0005
## 140 0.3083 nan 0.1000 -0.0003
## 160 0.2794 nan 0.1000 -0.0004
## 180 0.2531 nan 0.1000 -0.0011
## 200 0.2295 nan 0.1000 -0.0005
## 220 0.2042 nan 0.1000 -0.0012
## 240 0.1846 nan 0.1000 0.0003
## 260 0.1665 nan 0.1000 -0.0002
## 280 0.1526 nan 0.1000 -0.0001
## 300 0.1383 nan 0.1000 -0.0001
## 320 0.1279 nan 0.1000 -0.0002
## 340 0.1177 nan 0.1000 -0.0006
## 360 0.1093 nan 0.1000 -0.0003
## 380 0.0995 nan 0.1000 -0.0005
## 400 0.0913 nan 0.1000 -0.0003
## 420 0.0843 nan 0.1000 -0.0001
## 440 0.0790 nan 0.1000 -0.0001
## 460 0.0726 nan 0.1000 0.0000
## 480 0.0667 nan 0.1000 -0.0001
## 500 0.0614 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0393
## 2 1.1630 nan 0.1000 0.0292
## 3 1.1049 nan 0.1000 0.0266
## 4 1.0584 nan 0.1000 0.0212
## 5 1.0144 nan 0.1000 0.0192
## 6 0.9793 nan 0.1000 0.0152
## 7 0.9469 nan 0.1000 0.0131
## 8 0.9194 nan 0.1000 0.0112
## 9 0.8943 nan 0.1000 0.0110
## 10 0.8719 nan 0.1000 0.0097
## 20 0.7220 nan 0.1000 0.0018
## 40 0.5906 nan 0.1000 -0.0009
## 60 0.5067 nan 0.1000 -0.0007
## 80 0.4486 nan 0.1000 -0.0002
## 100 0.3989 nan 0.1000 -0.0011
## 120 0.3550 nan 0.1000 -0.0006
## 140 0.3216 nan 0.1000 -0.0005
## 160 0.2877 nan 0.1000 -0.0001
## 180 0.2637 nan 0.1000 -0.0009
## 200 0.2403 nan 0.1000 -0.0004
## 220 0.2203 nan 0.1000 -0.0003
## 240 0.1983 nan 0.1000 -0.0009
## 260 0.1816 nan 0.1000 -0.0004
## 280 0.1648 nan 0.1000 -0.0001
## 300 0.1503 nan 0.1000 -0.0006
## 320 0.1389 nan 0.1000 -0.0007
## 340 0.1274 nan 0.1000 -0.0002
## 360 0.1183 nan 0.1000 -0.0002
## 380 0.1096 nan 0.1000 -0.0003
## 400 0.1016 nan 0.1000 -0.0004
## 420 0.0944 nan 0.1000 -0.0007
## 440 0.0870 nan 0.1000 -0.0004
## 460 0.0807 nan 0.1000 0.0001
## 480 0.0747 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0374
## 2 1.1726 nan 0.1000 0.0339
## 3 1.1180 nan 0.1000 0.0255
## 4 1.0736 nan 0.1000 0.0196
## 5 1.0306 nan 0.1000 0.0191
## 6 0.9940 nan 0.1000 0.0157
## 7 0.9576 nan 0.1000 0.0140
## 8 0.9298 nan 0.1000 0.0104
## 9 0.8999 nan 0.1000 0.0106
## 10 0.8768 nan 0.1000 0.0102
## 20 0.7321 nan 0.1000 0.0013
## 40 0.6123 nan 0.1000 0.0004
## 60 0.5358 nan 0.1000 -0.0033
## 80 0.4672 nan 0.1000 -0.0004
## 100 0.4119 nan 0.1000 -0.0008
## 120 0.3707 nan 0.1000 -0.0003
## 140 0.3345 nan 0.1000 -0.0006
## 160 0.3012 nan 0.1000 -0.0009
## 180 0.2732 nan 0.1000 -0.0007
## 200 0.2487 nan 0.1000 -0.0007
## 220 0.2282 nan 0.1000 -0.0006
## 240 0.2120 nan 0.1000 -0.0006
## 260 0.1950 nan 0.1000 -0.0008
## 280 0.1784 nan 0.1000 -0.0004
## 300 0.1651 nan 0.1000 -0.0003
## 320 0.1519 nan 0.1000 -0.0004
## 340 0.1397 nan 0.1000 -0.0004
## 360 0.1286 nan 0.1000 -0.0003
## 380 0.1187 nan 0.1000 -0.0004
## 400 0.1101 nan 0.1000 -0.0004
## 420 0.1011 nan 0.1000 -0.0006
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## 460 0.0873 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2291 nan 0.1000 0.0392
## 2 1.1582 nan 0.1000 0.0350
## 3 1.1002 nan 0.1000 0.0249
## 4 1.0481 nan 0.1000 0.0213
## 5 1.0032 nan 0.1000 0.0181
## 6 0.9614 nan 0.1000 0.0179
## 7 0.9210 nan 0.1000 0.0164
## 8 0.8883 nan 0.1000 0.0138
## 9 0.8563 nan 0.1000 0.0139
## 10 0.8318 nan 0.1000 0.0117
## 20 0.6736 nan 0.1000 0.0027
## 40 0.5299 nan 0.1000 -0.0014
## 60 0.4375 nan 0.1000 -0.0014
## 80 0.3740 nan 0.1000 0.0004
## 100 0.3244 nan 0.1000 -0.0021
## 120 0.2793 nan 0.1000 -0.0007
## 140 0.2432 nan 0.1000 -0.0000
## 160 0.2140 nan 0.1000 -0.0001
## 180 0.1906 nan 0.1000 -0.0005
## 200 0.1679 nan 0.1000 -0.0003
## 220 0.1488 nan 0.1000 -0.0001
## 240 0.1327 nan 0.1000 -0.0000
## 260 0.1172 nan 0.1000 -0.0003
## 280 0.1050 nan 0.1000 0.0000
## 300 0.0957 nan 0.1000 -0.0001
## 320 0.0866 nan 0.1000 -0.0003
## 340 0.0780 nan 0.1000 -0.0001
## 360 0.0704 nan 0.1000 -0.0001
## 380 0.0635 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 -0.0001
## 420 0.0523 nan 0.1000 -0.0001
## 440 0.0473 nan 0.1000 -0.0001
## 460 0.0430 nan 0.1000 -0.0002
## 480 0.0387 nan 0.1000 -0.0002
## 500 0.0350 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2350 nan 0.1000 0.0414
## 2 1.1585 nan 0.1000 0.0321
## 3 1.0995 nan 0.1000 0.0281
## 4 1.0494 nan 0.1000 0.0204
## 5 1.0084 nan 0.1000 0.0175
## 6 0.9715 nan 0.1000 0.0183
## 7 0.9372 nan 0.1000 0.0138
## 8 0.9048 nan 0.1000 0.0127
## 9 0.8754 nan 0.1000 0.0113
## 10 0.8474 nan 0.1000 0.0124
## 20 0.6965 nan 0.1000 0.0024
## 40 0.5512 nan 0.1000 0.0001
## 60 0.4637 nan 0.1000 -0.0009
## 80 0.3966 nan 0.1000 -0.0009
## 100 0.3408 nan 0.1000 -0.0007
## 120 0.2996 nan 0.1000 -0.0012
## 140 0.2620 nan 0.1000 -0.0004
## 160 0.2272 nan 0.1000 -0.0002
## 180 0.2000 nan 0.1000 -0.0013
## 200 0.1790 nan 0.1000 -0.0007
## 220 0.1602 nan 0.1000 -0.0005
## 240 0.1441 nan 0.1000 -0.0005
## 260 0.1278 nan 0.1000 -0.0004
## 280 0.1158 nan 0.1000 -0.0005
## 300 0.1037 nan 0.1000 -0.0002
## 320 0.0939 nan 0.1000 -0.0003
## 340 0.0846 nan 0.1000 -0.0002
## 360 0.0760 nan 0.1000 -0.0004
## 380 0.0692 nan 0.1000 -0.0002
## 400 0.0618 nan 0.1000 -0.0000
## 420 0.0560 nan 0.1000 -0.0001
## 440 0.0509 nan 0.1000 -0.0002
## 460 0.0456 nan 0.1000 -0.0001
## 480 0.0412 nan 0.1000 -0.0002
## 500 0.0373 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2325 nan 0.1000 0.0383
## 2 1.1511 nan 0.1000 0.0335
## 3 1.0909 nan 0.1000 0.0261
## 4 1.0436 nan 0.1000 0.0236
## 5 1.0025 nan 0.1000 0.0175
## 6 0.9615 nan 0.1000 0.0174
## 7 0.9295 nan 0.1000 0.0132
## 8 0.8985 nan 0.1000 0.0121
## 9 0.8732 nan 0.1000 0.0098
## 10 0.8449 nan 0.1000 0.0107
## 20 0.6955 nan 0.1000 0.0022
## 40 0.5615 nan 0.1000 -0.0002
## 60 0.4828 nan 0.1000 -0.0014
## 80 0.4142 nan 0.1000 -0.0008
## 100 0.3555 nan 0.1000 0.0000
## 120 0.3105 nan 0.1000 -0.0010
## 140 0.2753 nan 0.1000 -0.0012
## 160 0.2436 nan 0.1000 -0.0009
## 180 0.2173 nan 0.1000 -0.0003
## 200 0.1953 nan 0.1000 -0.0000
## 220 0.1713 nan 0.1000 -0.0003
## 240 0.1529 nan 0.1000 -0.0004
## 260 0.1367 nan 0.1000 -0.0000
## 280 0.1222 nan 0.1000 -0.0003
## 300 0.1110 nan 0.1000 -0.0006
## 320 0.1010 nan 0.1000 -0.0007
## 340 0.0909 nan 0.1000 -0.0003
## 360 0.0833 nan 0.1000 -0.0001
## 380 0.0754 nan 0.1000 -0.0002
## 400 0.0682 nan 0.1000 -0.0003
## 420 0.0621 nan 0.1000 -0.0004
## 440 0.0561 nan 0.1000 -0.0003
## 460 0.0510 nan 0.1000 -0.0002
## 480 0.0468 nan 0.1000 -0.0002
## 500 0.0427 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.1000 0.0439
## 2 1.1514 nan 0.1000 0.0352
## 3 1.0846 nan 0.1000 0.0292
## 4 1.0275 nan 0.1000 0.0237
## 5 0.9821 nan 0.1000 0.0195
## 6 0.9369 nan 0.1000 0.0175
## 7 0.9041 nan 0.1000 0.0105
## 8 0.8735 nan 0.1000 0.0105
## 9 0.8428 nan 0.1000 0.0104
## 10 0.8193 nan 0.1000 0.0068
## 20 0.6568 nan 0.1000 0.0020
## 40 0.4914 nan 0.1000 0.0008
## 60 0.4047 nan 0.1000 -0.0019
## 80 0.3360 nan 0.1000 -0.0006
## 100 0.2801 nan 0.1000 -0.0009
## 120 0.2355 nan 0.1000 -0.0007
## 140 0.2027 nan 0.1000 -0.0005
## 160 0.1725 nan 0.1000 -0.0007
## 180 0.1501 nan 0.1000 -0.0001
## 200 0.1315 nan 0.1000 -0.0001
## 220 0.1154 nan 0.1000 -0.0000
## 240 0.0995 nan 0.1000 -0.0002
## 260 0.0851 nan 0.1000 0.0001
## 280 0.0751 nan 0.1000 -0.0003
## 300 0.0650 nan 0.1000 -0.0000
## 320 0.0576 nan 0.1000 -0.0002
## 340 0.0512 nan 0.1000 -0.0000
## 360 0.0449 nan 0.1000 -0.0000
## 380 0.0393 nan 0.1000 -0.0001
## 400 0.0348 nan 0.1000 -0.0000
## 420 0.0312 nan 0.1000 -0.0000
## 440 0.0277 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0001
## 500 0.0194 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0366
## 2 1.1574 nan 0.1000 0.0348
## 3 1.0967 nan 0.1000 0.0291
## 4 1.0447 nan 0.1000 0.0251
## 5 0.9985 nan 0.1000 0.0199
## 6 0.9577 nan 0.1000 0.0181
## 7 0.9219 nan 0.1000 0.0144
## 8 0.8901 nan 0.1000 0.0121
## 9 0.8653 nan 0.1000 0.0091
## 10 0.8386 nan 0.1000 0.0104
## 20 0.6714 nan 0.1000 0.0021
## 40 0.5116 nan 0.1000 -0.0019
## 60 0.4142 nan 0.1000 0.0001
## 80 0.3401 nan 0.1000 0.0010
## 100 0.2891 nan 0.1000 -0.0014
## 120 0.2475 nan 0.1000 -0.0005
## 140 0.2132 nan 0.1000 -0.0008
## 160 0.1815 nan 0.1000 -0.0008
## 180 0.1580 nan 0.1000 -0.0004
## 200 0.1355 nan 0.1000 -0.0003
## 220 0.1184 nan 0.1000 -0.0005
## 240 0.1038 nan 0.1000 -0.0004
## 260 0.0913 nan 0.1000 -0.0003
## 280 0.0802 nan 0.1000 -0.0004
## 300 0.0711 nan 0.1000 -0.0005
## 320 0.0626 nan 0.1000 -0.0001
## 340 0.0552 nan 0.1000 -0.0001
## 360 0.0494 nan 0.1000 -0.0002
## 380 0.0435 nan 0.1000 -0.0001
## 400 0.0385 nan 0.1000 -0.0001
## 420 0.0345 nan 0.1000 -0.0001
## 440 0.0307 nan 0.1000 -0.0000
## 460 0.0276 nan 0.1000 -0.0001
## 480 0.0244 nan 0.1000 -0.0001
## 500 0.0220 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2301 nan 0.1000 0.0381
## 2 1.1508 nan 0.1000 0.0316
## 3 1.0905 nan 0.1000 0.0249
## 4 1.0317 nan 0.1000 0.0238
## 5 0.9916 nan 0.1000 0.0170
## 6 0.9547 nan 0.1000 0.0154
## 7 0.9219 nan 0.1000 0.0145
## 8 0.8917 nan 0.1000 0.0125
## 9 0.8612 nan 0.1000 0.0099
## 10 0.8355 nan 0.1000 0.0095
## 20 0.6814 nan 0.1000 -0.0003
## 40 0.5344 nan 0.1000 0.0003
## 60 0.4413 nan 0.1000 -0.0021
## 80 0.3684 nan 0.1000 -0.0016
## 100 0.3131 nan 0.1000 -0.0007
## 120 0.2670 nan 0.1000 -0.0003
## 140 0.2287 nan 0.1000 -0.0014
## 160 0.1982 nan 0.1000 -0.0008
## 180 0.1719 nan 0.1000 -0.0007
## 200 0.1485 nan 0.1000 -0.0004
## 220 0.1296 nan 0.1000 -0.0010
## 240 0.1136 nan 0.1000 -0.0006
## 260 0.0999 nan 0.1000 -0.0006
## 280 0.0885 nan 0.1000 -0.0006
## 300 0.0781 nan 0.1000 -0.0002
## 320 0.0688 nan 0.1000 -0.0003
## 340 0.0613 nan 0.1000 -0.0003
## 360 0.0550 nan 0.1000 -0.0001
## 380 0.0489 nan 0.1000 -0.0002
## 400 0.0433 nan 0.1000 -0.0001
## 420 0.0388 nan 0.1000 -0.0001
## 440 0.0346 nan 0.1000 -0.0002
## 460 0.0312 nan 0.1000 -0.0001
## 480 0.0281 nan 0.1000 -0.0001
## 500 0.0251 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2572 nan 0.0010 0.0004
## 100 1.2427 nan 0.0010 0.0003
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2150 nan 0.0010 0.0003
## 160 1.2017 nan 0.0010 0.0003
## 180 1.1888 nan 0.0010 0.0003
## 200 1.1765 nan 0.0010 0.0002
## 220 1.1647 nan 0.0010 0.0002
## 240 1.1529 nan 0.0010 0.0002
## 260 1.1414 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1197 nan 0.0010 0.0002
## 320 1.1093 nan 0.0010 0.0002
## 340 1.0992 nan 0.0010 0.0002
## 360 1.0892 nan 0.0010 0.0002
## 380 1.0795 nan 0.0010 0.0002
## 400 1.0700 nan 0.0010 0.0002
## 420 1.0610 nan 0.0010 0.0002
## 440 1.0521 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0349 nan 0.0010 0.0002
## 500 1.0268 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2726 nan 0.0010 0.0004
## 80 1.2576 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2152 nan 0.0010 0.0003
## 160 1.2022 nan 0.0010 0.0003
## 180 1.1895 nan 0.0010 0.0003
## 200 1.1770 nan 0.0010 0.0003
## 220 1.1650 nan 0.0010 0.0003
## 240 1.1532 nan 0.0010 0.0003
## 260 1.1419 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1200 nan 0.0010 0.0002
## 320 1.1097 nan 0.0010 0.0002
## 340 1.0994 nan 0.0010 0.0003
## 360 1.0894 nan 0.0010 0.0002
## 380 1.0799 nan 0.0010 0.0002
## 400 1.0705 nan 0.0010 0.0002
## 420 1.0611 nan 0.0010 0.0002
## 440 1.0522 nan 0.0010 0.0002
## 460 1.0438 nan 0.0010 0.0002
## 480 1.0356 nan 0.0010 0.0002
## 500 1.0273 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3165 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2579 nan 0.0010 0.0004
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2296 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2031 nan 0.0010 0.0003
## 180 1.1906 nan 0.0010 0.0003
## 200 1.1782 nan 0.0010 0.0003
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1548 nan 0.0010 0.0003
## 260 1.1436 nan 0.0010 0.0002
## 280 1.1327 nan 0.0010 0.0002
## 300 1.1219 nan 0.0010 0.0002
## 320 1.1114 nan 0.0010 0.0002
## 340 1.1012 nan 0.0010 0.0002
## 360 1.0914 nan 0.0010 0.0002
## 380 1.0818 nan 0.0010 0.0002
## 400 1.0724 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
## 440 1.0545 nan 0.0010 0.0002
## 460 1.0458 nan 0.0010 0.0002
## 480 1.0375 nan 0.0010 0.0002
## 500 1.0291 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2526 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0003
## 120 1.2224 nan 0.0010 0.0003
## 140 1.2076 nan 0.0010 0.0003
## 160 1.1939 nan 0.0010 0.0003
## 180 1.1804 nan 0.0010 0.0003
## 200 1.1673 nan 0.0010 0.0003
## 220 1.1543 nan 0.0010 0.0003
## 240 1.1420 nan 0.0010 0.0003
## 260 1.1302 nan 0.0010 0.0003
## 280 1.1188 nan 0.0010 0.0002
## 300 1.1071 nan 0.0010 0.0003
## 320 1.0961 nan 0.0010 0.0002
## 340 1.0855 nan 0.0010 0.0002
## 360 1.0748 nan 0.0010 0.0002
## 380 1.0644 nan 0.0010 0.0002
## 400 1.0544 nan 0.0010 0.0002
## 420 1.0447 nan 0.0010 0.0002
## 440 1.0352 nan 0.0010 0.0002
## 460 1.0263 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0089 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0003
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2373 nan 0.0010 0.0003
## 120 1.2222 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1943 nan 0.0010 0.0003
## 180 1.1807 nan 0.0010 0.0002
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0003
## 240 1.1419 nan 0.0010 0.0002
## 260 1.1295 nan 0.0010 0.0003
## 280 1.1179 nan 0.0010 0.0003
## 300 1.1066 nan 0.0010 0.0002
## 320 1.0956 nan 0.0010 0.0002
## 340 1.0852 nan 0.0010 0.0002
## 360 1.0749 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0548 nan 0.0010 0.0002
## 420 1.0450 nan 0.0010 0.0002
## 440 1.0356 nan 0.0010 0.0002
## 460 1.0265 nan 0.0010 0.0002
## 480 1.0176 nan 0.0010 0.0002
## 500 1.0087 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0003
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0003
## 100 1.2390 nan 0.0010 0.0003
## 120 1.2246 nan 0.0010 0.0004
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0002
## 200 1.1697 nan 0.0010 0.0003
## 220 1.1570 nan 0.0010 0.0003
## 240 1.1448 nan 0.0010 0.0003
## 260 1.1331 nan 0.0010 0.0003
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1102 nan 0.0010 0.0002
## 320 1.0993 nan 0.0010 0.0002
## 340 1.0887 nan 0.0010 0.0002
## 360 1.0783 nan 0.0010 0.0002
## 380 1.0682 nan 0.0010 0.0002
## 400 1.0585 nan 0.0010 0.0002
## 420 1.0489 nan 0.0010 0.0002
## 440 1.0393 nan 0.0010 0.0002
## 460 1.0303 nan 0.0010 0.0002
## 480 1.0218 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0003
## 100 1.2332 nan 0.0010 0.0004
## 120 1.2173 nan 0.0010 0.0003
## 140 1.2022 nan 0.0010 0.0003
## 160 1.1875 nan 0.0010 0.0003
## 180 1.1729 nan 0.0010 0.0004
## 200 1.1591 nan 0.0010 0.0003
## 220 1.1457 nan 0.0010 0.0003
## 240 1.1324 nan 0.0010 0.0003
## 260 1.1200 nan 0.0010 0.0003
## 280 1.1079 nan 0.0010 0.0003
## 300 1.0960 nan 0.0010 0.0002
## 320 1.0845 nan 0.0010 0.0003
## 340 1.0731 nan 0.0010 0.0002
## 360 1.0625 nan 0.0010 0.0002
## 380 1.0519 nan 0.0010 0.0002
## 400 1.0414 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0217 nan 0.0010 0.0002
## 460 1.0122 nan 0.0010 0.0002
## 480 1.0027 nan 0.0010 0.0002
## 500 0.9938 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2668 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2032 nan 0.0010 0.0003
## 160 1.1887 nan 0.0010 0.0003
## 180 1.1745 nan 0.0010 0.0003
## 200 1.1605 nan 0.0010 0.0003
## 220 1.1474 nan 0.0010 0.0003
## 240 1.1345 nan 0.0010 0.0003
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## 280 1.1102 nan 0.0010 0.0002
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0870 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0543 nan 0.0010 0.0002
## 400 1.0440 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0242 nan 0.0010 0.0002
## 460 1.0149 nan 0.0010 0.0002
## 480 1.0056 nan 0.0010 0.0002
## 500 0.9967 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2361 nan 0.0010 0.0003
## 120 1.2208 nan 0.0010 0.0003
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1914 nan 0.0010 0.0003
## 180 1.1776 nan 0.0010 0.0003
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## 260 1.1257 nan 0.0010 0.0002
## 280 1.1138 nan 0.0010 0.0003
## 300 1.1023 nan 0.0010 0.0002
## 320 1.0910 nan 0.0010 0.0002
## 340 1.0800 nan 0.0010 0.0002
## 360 1.0696 nan 0.0010 0.0002
## 380 1.0590 nan 0.0010 0.0002
## 400 1.0489 nan 0.0010 0.0002
## 420 1.0390 nan 0.0010 0.0002
## 440 1.0296 nan 0.0010 0.0002
## 460 1.0201 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3046 nan 0.0100 0.0038
## 3 1.2969 nan 0.0100 0.0037
## 4 1.2886 nan 0.0100 0.0036
## 5 1.2809 nan 0.0100 0.0039
## 6 1.2731 nan 0.0100 0.0039
## 7 1.2656 nan 0.0100 0.0034
## 8 1.2582 nan 0.0100 0.0034
## 9 1.2510 nan 0.0100 0.0031
## 10 1.2440 nan 0.0100 0.0033
## 20 1.1780 nan 0.0100 0.0032
## 40 1.0692 nan 0.0100 0.0020
## 60 0.9863 nan 0.0100 0.0012
## 80 0.9228 nan 0.0100 0.0012
## 100 0.8716 nan 0.0100 0.0009
## 120 0.8302 nan 0.0100 0.0005
## 140 0.7957 nan 0.0100 0.0002
## 160 0.7674 nan 0.0100 0.0006
## 180 0.7415 nan 0.0100 0.0003
## 200 0.7195 nan 0.0100 0.0001
## 220 0.7003 nan 0.0100 -0.0001
## 240 0.6829 nan 0.0100 0.0001
## 260 0.6663 nan 0.0100 -0.0000
## 280 0.6514 nan 0.0100 -0.0000
## 300 0.6387 nan 0.0100 -0.0001
## 320 0.6262 nan 0.0100 0.0000
## 340 0.6156 nan 0.0100 -0.0000
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## 380 0.5948 nan 0.0100 -0.0001
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## 420 0.5751 nan 0.0100 0.0001
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## 460 0.5578 nan 0.0100 -0.0002
## 480 0.5494 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3041 nan 0.0100 0.0038
## 3 1.2953 nan 0.0100 0.0037
## 4 1.2871 nan 0.0100 0.0037
## 5 1.2791 nan 0.0100 0.0037
## 6 1.2718 nan 0.0100 0.0035
## 7 1.2637 nan 0.0100 0.0035
## 8 1.2557 nan 0.0100 0.0037
## 9 1.2483 nan 0.0100 0.0033
## 10 1.2413 nan 0.0100 0.0034
## 20 1.1759 nan 0.0100 0.0024
## 40 1.0711 nan 0.0100 0.0023
## 60 0.9888 nan 0.0100 0.0012
## 80 0.9243 nan 0.0100 0.0010
## 100 0.8730 nan 0.0100 0.0009
## 120 0.8319 nan 0.0100 0.0009
## 140 0.7970 nan 0.0100 0.0003
## 160 0.7680 nan 0.0100 0.0005
## 180 0.7433 nan 0.0100 0.0003
## 200 0.7212 nan 0.0100 0.0002
## 220 0.7034 nan 0.0100 0.0001
## 240 0.6864 nan 0.0100 0.0002
## 260 0.6715 nan 0.0100 0.0001
## 280 0.6584 nan 0.0100 0.0000
## 300 0.6456 nan 0.0100 0.0000
## 320 0.6340 nan 0.0100 0.0000
## 340 0.6233 nan 0.0100 -0.0001
## 360 0.6132 nan 0.0100 0.0000
## 380 0.6030 nan 0.0100 -0.0001
## 400 0.5929 nan 0.0100 -0.0000
## 420 0.5851 nan 0.0100 -0.0001
## 440 0.5760 nan 0.0100 0.0000
## 460 0.5685 nan 0.0100 0.0000
## 480 0.5601 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3046 nan 0.0100 0.0035
## 3 1.2963 nan 0.0100 0.0037
## 4 1.2881 nan 0.0100 0.0038
## 5 1.2805 nan 0.0100 0.0031
## 6 1.2723 nan 0.0100 0.0037
## 7 1.2651 nan 0.0100 0.0033
## 8 1.2575 nan 0.0100 0.0033
## 9 1.2506 nan 0.0100 0.0031
## 10 1.2434 nan 0.0100 0.0029
## 20 1.1769 nan 0.0100 0.0029
## 40 1.0703 nan 0.0100 0.0021
## 60 0.9889 nan 0.0100 0.0015
## 80 0.9268 nan 0.0100 0.0013
## 100 0.8775 nan 0.0100 0.0006
## 120 0.8345 nan 0.0100 0.0008
## 140 0.8018 nan 0.0100 0.0002
## 160 0.7730 nan 0.0100 0.0000
## 180 0.7485 nan 0.0100 0.0003
## 200 0.7268 nan 0.0100 0.0003
## 220 0.7080 nan 0.0100 0.0002
## 240 0.6928 nan 0.0100 0.0001
## 260 0.6776 nan 0.0100 0.0002
## 280 0.6637 nan 0.0100 0.0000
## 300 0.6513 nan 0.0100 0.0000
## 320 0.6405 nan 0.0100 0.0000
## 340 0.6300 nan 0.0100 -0.0001
## 360 0.6204 nan 0.0100 -0.0001
## 380 0.6102 nan 0.0100 -0.0001
## 400 0.6014 nan 0.0100 -0.0002
## 420 0.5917 nan 0.0100 0.0000
## 440 0.5828 nan 0.0100 -0.0000
## 460 0.5740 nan 0.0100 -0.0001
## 480 0.5648 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0036
## 2 1.3030 nan 0.0100 0.0038
## 3 1.2944 nan 0.0100 0.0037
## 4 1.2856 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0039
## 6 1.2690 nan 0.0100 0.0033
## 7 1.2602 nan 0.0100 0.0038
## 8 1.2516 nan 0.0100 0.0039
## 9 1.2434 nan 0.0100 0.0036
## 10 1.2360 nan 0.0100 0.0033
## 20 1.1668 nan 0.0100 0.0032
## 40 1.0563 nan 0.0100 0.0019
## 60 0.9712 nan 0.0100 0.0012
## 80 0.9033 nan 0.0100 0.0013
## 100 0.8493 nan 0.0100 0.0009
## 120 0.8056 nan 0.0100 0.0007
## 140 0.7695 nan 0.0100 0.0005
## 160 0.7392 nan 0.0100 0.0003
## 180 0.7120 nan 0.0100 0.0002
## 200 0.6889 nan 0.0100 0.0003
## 220 0.6681 nan 0.0100 0.0003
## 240 0.6495 nan 0.0100 -0.0000
## 260 0.6312 nan 0.0100 0.0001
## 280 0.6156 nan 0.0100 0.0000
## 300 0.6008 nan 0.0100 -0.0000
## 320 0.5876 nan 0.0100 -0.0001
## 340 0.5749 nan 0.0100 -0.0000
## 360 0.5633 nan 0.0100 -0.0000
## 380 0.5512 nan 0.0100 0.0001
## 400 0.5396 nan 0.0100 -0.0000
## 420 0.5294 nan 0.0100 -0.0001
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## 460 0.5090 nan 0.0100 -0.0000
## 480 0.5002 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0035
## 2 1.3037 nan 0.0100 0.0042
## 3 1.2947 nan 0.0100 0.0043
## 4 1.2865 nan 0.0100 0.0033
## 5 1.2789 nan 0.0100 0.0035
## 6 1.2707 nan 0.0100 0.0036
## 7 1.2625 nan 0.0100 0.0039
## 8 1.2552 nan 0.0100 0.0032
## 9 1.2475 nan 0.0100 0.0032
## 10 1.2400 nan 0.0100 0.0031
## 20 1.1696 nan 0.0100 0.0030
## 40 1.0545 nan 0.0100 0.0022
## 60 0.9682 nan 0.0100 0.0013
## 80 0.9008 nan 0.0100 0.0011
## 100 0.8472 nan 0.0100 0.0010
## 120 0.8037 nan 0.0100 0.0005
## 140 0.7672 nan 0.0100 0.0006
## 160 0.7375 nan 0.0100 0.0001
## 180 0.7113 nan 0.0100 0.0002
## 200 0.6904 nan 0.0100 0.0003
## 220 0.6695 nan 0.0100 0.0001
## 240 0.6509 nan 0.0100 0.0001
## 260 0.6346 nan 0.0100 0.0000
## 280 0.6190 nan 0.0100 0.0000
## 300 0.6052 nan 0.0100 -0.0001
## 320 0.5906 nan 0.0100 0.0002
## 340 0.5779 nan 0.0100 0.0000
## 360 0.5668 nan 0.0100 0.0000
## 380 0.5555 nan 0.0100 -0.0001
## 400 0.5443 nan 0.0100 -0.0001
## 420 0.5340 nan 0.0100 -0.0001
## 440 0.5239 nan 0.0100 0.0001
## 460 0.5135 nan 0.0100 -0.0000
## 480 0.5045 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0040
## 2 1.3022 nan 0.0100 0.0036
## 3 1.2944 nan 0.0100 0.0036
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2776 nan 0.0100 0.0033
## 6 1.2704 nan 0.0100 0.0032
## 7 1.2621 nan 0.0100 0.0034
## 8 1.2543 nan 0.0100 0.0034
## 9 1.2465 nan 0.0100 0.0038
## 10 1.2394 nan 0.0100 0.0033
## 20 1.1711 nan 0.0100 0.0030
## 40 1.0624 nan 0.0100 0.0024
## 60 0.9794 nan 0.0100 0.0015
## 80 0.9110 nan 0.0100 0.0012
## 100 0.8564 nan 0.0100 0.0011
## 120 0.8135 nan 0.0100 0.0006
## 140 0.7772 nan 0.0100 0.0003
## 160 0.7476 nan 0.0100 0.0004
## 180 0.7209 nan 0.0100 0.0003
## 200 0.6985 nan 0.0100 0.0004
## 220 0.6794 nan 0.0100 0.0001
## 240 0.6613 nan 0.0100 0.0003
## 260 0.6450 nan 0.0100 0.0000
## 280 0.6302 nan 0.0100 -0.0001
## 300 0.6170 nan 0.0100 0.0001
## 320 0.6044 nan 0.0100 -0.0000
## 340 0.5922 nan 0.0100 -0.0001
## 360 0.5808 nan 0.0100 -0.0000
## 380 0.5710 nan 0.0100 -0.0000
## 400 0.5607 nan 0.0100 -0.0002
## 420 0.5507 nan 0.0100 0.0000
## 440 0.5408 nan 0.0100 -0.0000
## 460 0.5318 nan 0.0100 -0.0001
## 480 0.5220 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0044
## 2 1.3020 nan 0.0100 0.0039
## 3 1.2940 nan 0.0100 0.0036
## 4 1.2844 nan 0.0100 0.0043
## 5 1.2751 nan 0.0100 0.0043
## 6 1.2668 nan 0.0100 0.0039
## 7 1.2582 nan 0.0100 0.0040
## 8 1.2498 nan 0.0100 0.0034
## 9 1.2413 nan 0.0100 0.0040
## 10 1.2332 nan 0.0100 0.0036
## 20 1.1594 nan 0.0100 0.0031
## 40 1.0430 nan 0.0100 0.0020
## 60 0.9508 nan 0.0100 0.0016
## 80 0.8821 nan 0.0100 0.0011
## 100 0.8247 nan 0.0100 0.0010
## 120 0.7788 nan 0.0100 0.0007
## 140 0.7406 nan 0.0100 0.0003
## 160 0.7073 nan 0.0100 0.0005
## 180 0.6792 nan 0.0100 0.0002
## 200 0.6530 nan 0.0100 0.0003
## 220 0.6305 nan 0.0100 0.0001
## 240 0.6091 nan 0.0100 0.0001
## 260 0.5911 nan 0.0100 -0.0000
## 280 0.5747 nan 0.0100 0.0002
## 300 0.5600 nan 0.0100 -0.0000
## 320 0.5455 nan 0.0100 -0.0000
## 340 0.5323 nan 0.0100 0.0001
## 360 0.5186 nan 0.0100 0.0000
## 380 0.5071 nan 0.0100 0.0001
## 400 0.4947 nan 0.0100 0.0000
## 420 0.4826 nan 0.0100 0.0001
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## 460 0.4633 nan 0.0100 0.0000
## 480 0.4538 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3103 nan 0.0100 0.0044
## 2 1.3016 nan 0.0100 0.0042
## 3 1.2922 nan 0.0100 0.0043
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2742 nan 0.0100 0.0043
## 6 1.2655 nan 0.0100 0.0041
## 7 1.2568 nan 0.0100 0.0040
## 8 1.2485 nan 0.0100 0.0031
## 9 1.2401 nan 0.0100 0.0037
## 10 1.2326 nan 0.0100 0.0031
## 20 1.1616 nan 0.0100 0.0028
## 40 1.0441 nan 0.0100 0.0019
## 60 0.9555 nan 0.0100 0.0017
## 80 0.8859 nan 0.0100 0.0012
## 100 0.8278 nan 0.0100 0.0009
## 120 0.7823 nan 0.0100 0.0005
## 140 0.7434 nan 0.0100 0.0003
## 160 0.7108 nan 0.0100 0.0005
## 180 0.6853 nan 0.0100 0.0004
## 200 0.6603 nan 0.0100 0.0002
## 220 0.6388 nan 0.0100 0.0004
## 240 0.6192 nan 0.0100 0.0001
## 260 0.6009 nan 0.0100 0.0001
## 280 0.5851 nan 0.0100 0.0002
## 300 0.5703 nan 0.0100 -0.0001
## 320 0.5570 nan 0.0100 0.0000
## 340 0.5439 nan 0.0100 0.0000
## 360 0.5313 nan 0.0100 0.0000
## 380 0.5190 nan 0.0100 -0.0001
## 400 0.5066 nan 0.0100 0.0001
## 420 0.4956 nan 0.0100 -0.0000
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## 460 0.4743 nan 0.0100 -0.0000
## 480 0.4638 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0040
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2862 nan 0.0100 0.0037
## 5 1.2771 nan 0.0100 0.0040
## 6 1.2684 nan 0.0100 0.0039
## 7 1.2604 nan 0.0100 0.0035
## 8 1.2520 nan 0.0100 0.0036
## 9 1.2452 nan 0.0100 0.0033
## 10 1.2373 nan 0.0100 0.0034
## 20 1.1655 nan 0.0100 0.0029
## 40 1.0512 nan 0.0100 0.0022
## 60 0.9640 nan 0.0100 0.0014
## 80 0.8955 nan 0.0100 0.0011
## 100 0.8401 nan 0.0100 0.0010
## 120 0.7951 nan 0.0100 0.0008
## 140 0.7582 nan 0.0100 0.0004
## 160 0.7263 nan 0.0100 0.0003
## 180 0.6989 nan 0.0100 0.0003
## 200 0.6759 nan 0.0100 0.0001
## 220 0.6552 nan 0.0100 0.0001
## 240 0.6349 nan 0.0100 -0.0000
## 260 0.6179 nan 0.0100 0.0001
## 280 0.6022 nan 0.0100 -0.0000
## 300 0.5870 nan 0.0100 0.0001
## 320 0.5723 nan 0.0100 0.0001
## 340 0.5586 nan 0.0100 -0.0001
## 360 0.5459 nan 0.0100 0.0001
## 380 0.5333 nan 0.0100 0.0002
## 400 0.5210 nan 0.0100 -0.0000
## 420 0.5093 nan 0.0100 -0.0001
## 440 0.4979 nan 0.0100 0.0000
## 460 0.4869 nan 0.0100 0.0001
## 480 0.4771 nan 0.0100 -0.0001
## 500 0.4666 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2388 nan 0.1000 0.0382
## 2 1.1687 nan 0.1000 0.0291
## 3 1.1093 nan 0.1000 0.0271
## 4 1.0617 nan 0.1000 0.0211
## 5 1.0170 nan 0.1000 0.0172
## 6 0.9818 nan 0.1000 0.0127
## 7 0.9496 nan 0.1000 0.0123
## 8 0.9178 nan 0.1000 0.0119
## 9 0.8953 nan 0.1000 0.0053
## 10 0.8682 nan 0.1000 0.0116
## 20 0.7182 nan 0.1000 0.0017
## 40 0.5925 nan 0.1000 -0.0002
## 60 0.5183 nan 0.1000 -0.0007
## 80 0.4438 nan 0.1000 -0.0010
## 100 0.3936 nan 0.1000 -0.0001
## 120 0.3578 nan 0.1000 -0.0002
## 140 0.3196 nan 0.1000 -0.0008
## 160 0.2872 nan 0.1000 -0.0008
## 180 0.2562 nan 0.1000 -0.0001
## 200 0.2286 nan 0.1000 -0.0000
## 220 0.2058 nan 0.1000 0.0001
## 240 0.1866 nan 0.1000 -0.0006
## 260 0.1690 nan 0.1000 -0.0001
## 280 0.1538 nan 0.1000 -0.0003
## 300 0.1420 nan 0.1000 -0.0001
## 320 0.1294 nan 0.1000 -0.0002
## 340 0.1167 nan 0.1000 -0.0004
## 360 0.1065 nan 0.1000 -0.0004
## 380 0.0978 nan 0.1000 -0.0000
## 400 0.0904 nan 0.1000 -0.0001
## 420 0.0833 nan 0.1000 -0.0002
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## 460 0.0713 nan 0.1000 -0.0002
## 480 0.0657 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2402 nan 0.1000 0.0364
## 2 1.1751 nan 0.1000 0.0302
## 3 1.1142 nan 0.1000 0.0263
## 4 1.0633 nan 0.1000 0.0221
## 5 1.0186 nan 0.1000 0.0178
## 6 0.9823 nan 0.1000 0.0146
## 7 0.9473 nan 0.1000 0.0137
## 8 0.9139 nan 0.1000 0.0128
## 9 0.8887 nan 0.1000 0.0092
## 10 0.8642 nan 0.1000 0.0112
## 20 0.7215 nan 0.1000 0.0001
## 40 0.5964 nan 0.1000 -0.0009
## 60 0.5168 nan 0.1000 -0.0008
## 80 0.4525 nan 0.1000 -0.0019
## 100 0.4022 nan 0.1000 -0.0012
## 120 0.3577 nan 0.1000 -0.0004
## 140 0.3258 nan 0.1000 -0.0002
## 160 0.2921 nan 0.1000 -0.0003
## 180 0.2600 nan 0.1000 -0.0004
## 200 0.2350 nan 0.1000 -0.0009
## 220 0.2131 nan 0.1000 -0.0004
## 240 0.1928 nan 0.1000 -0.0003
## 260 0.1755 nan 0.1000 -0.0003
## 280 0.1610 nan 0.1000 -0.0004
## 300 0.1464 nan 0.1000 -0.0003
## 320 0.1338 nan 0.1000 0.0003
## 340 0.1246 nan 0.1000 -0.0005
## 360 0.1135 nan 0.1000 -0.0002
## 380 0.1048 nan 0.1000 -0.0004
## 400 0.0958 nan 0.1000 -0.0001
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## 480 0.0699 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2382 nan 0.1000 0.0347
## 2 1.1718 nan 0.1000 0.0305
## 3 1.1166 nan 0.1000 0.0239
## 4 1.0610 nan 0.1000 0.0242
## 5 1.0176 nan 0.1000 0.0176
## 6 0.9864 nan 0.1000 0.0097
## 7 0.9560 nan 0.1000 0.0127
## 8 0.9293 nan 0.1000 0.0099
## 9 0.9029 nan 0.1000 0.0103
## 10 0.8837 nan 0.1000 0.0065
## 20 0.7272 nan 0.1000 0.0031
## 40 0.6028 nan 0.1000 -0.0032
## 60 0.5238 nan 0.1000 0.0004
## 80 0.4603 nan 0.1000 0.0011
## 100 0.4058 nan 0.1000 -0.0012
## 120 0.3628 nan 0.1000 -0.0004
## 140 0.3269 nan 0.1000 -0.0021
## 160 0.2920 nan 0.1000 -0.0009
## 180 0.2631 nan 0.1000 -0.0001
## 200 0.2392 nan 0.1000 -0.0005
## 220 0.2162 nan 0.1000 -0.0005
## 240 0.1951 nan 0.1000 -0.0005
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## 280 0.1607 nan 0.1000 -0.0004
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## 320 0.1373 nan 0.1000 -0.0006
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## 380 0.1064 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0401
## 2 1.1668 nan 0.1000 0.0276
## 3 1.1058 nan 0.1000 0.0220
## 4 1.0497 nan 0.1000 0.0232
## 5 1.0063 nan 0.1000 0.0170
## 6 0.9695 nan 0.1000 0.0128
## 7 0.9327 nan 0.1000 0.0151
## 8 0.9010 nan 0.1000 0.0126
## 9 0.8719 nan 0.1000 0.0108
## 10 0.8454 nan 0.1000 0.0100
## 20 0.6955 nan 0.1000 -0.0002
## 40 0.5435 nan 0.1000 0.0001
## 60 0.4553 nan 0.1000 -0.0008
## 80 0.3799 nan 0.1000 -0.0006
## 100 0.3265 nan 0.1000 0.0005
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## 140 0.2486 nan 0.1000 -0.0008
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## 180 0.1921 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2343 nan 0.1000 0.0394
## 2 1.1589 nan 0.1000 0.0332
## 3 1.1023 nan 0.1000 0.0266
## 4 1.0439 nan 0.1000 0.0236
## 5 0.9985 nan 0.1000 0.0187
## 6 0.9624 nan 0.1000 0.0154
## 7 0.9253 nan 0.1000 0.0148
## 8 0.8961 nan 0.1000 0.0127
## 9 0.8620 nan 0.1000 0.0129
## 10 0.8350 nan 0.1000 0.0115
## 20 0.6897 nan 0.1000 0.0039
## 40 0.5480 nan 0.1000 -0.0001
## 60 0.4652 nan 0.1000 0.0002
## 80 0.3909 nan 0.1000 -0.0007
## 100 0.3384 nan 0.1000 -0.0013
## 120 0.2911 nan 0.1000 -0.0009
## 140 0.2543 nan 0.1000 -0.0006
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## 180 0.1966 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0337
## 2 1.1764 nan 0.1000 0.0321
## 3 1.1071 nan 0.1000 0.0304
## 4 1.0576 nan 0.1000 0.0220
## 5 1.0138 nan 0.1000 0.0162
## 6 0.9752 nan 0.1000 0.0147
## 7 0.9395 nan 0.1000 0.0125
## 8 0.9079 nan 0.1000 0.0138
## 9 0.8780 nan 0.1000 0.0102
## 10 0.8508 nan 0.1000 0.0105
## 20 0.7052 nan 0.1000 0.0008
## 40 0.5637 nan 0.1000 -0.0000
## 60 0.4737 nan 0.1000 0.0001
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## 180 0.2113 nan 0.1000 -0.0014
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## 460 0.0452 nan 0.1000 -0.0001
## 480 0.0414 nan 0.1000 -0.0002
## 500 0.0375 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.1000 0.0407
## 2 1.1506 nan 0.1000 0.0322
## 3 1.0852 nan 0.1000 0.0296
## 4 1.0261 nan 0.1000 0.0241
## 5 0.9796 nan 0.1000 0.0198
## 6 0.9391 nan 0.1000 0.0160
## 7 0.9009 nan 0.1000 0.0155
## 8 0.8721 nan 0.1000 0.0107
## 9 0.8452 nan 0.1000 0.0077
## 10 0.8176 nan 0.1000 0.0081
## 20 0.6542 nan 0.1000 0.0026
## 40 0.5088 nan 0.1000 -0.0005
## 60 0.4113 nan 0.1000 -0.0011
## 80 0.3361 nan 0.1000 -0.0011
## 100 0.2860 nan 0.1000 -0.0004
## 120 0.2418 nan 0.1000 -0.0001
## 140 0.2055 nan 0.1000 0.0002
## 160 0.1771 nan 0.1000 -0.0006
## 180 0.1503 nan 0.1000 -0.0001
## 200 0.1298 nan 0.1000 -0.0004
## 220 0.1128 nan 0.1000 -0.0003
## 240 0.0986 nan 0.1000 -0.0004
## 260 0.0846 nan 0.1000 -0.0003
## 280 0.0740 nan 0.1000 -0.0001
## 300 0.0646 nan 0.1000 -0.0001
## 320 0.0563 nan 0.1000 -0.0001
## 340 0.0495 nan 0.1000 -0.0000
## 360 0.0437 nan 0.1000 -0.0001
## 380 0.0386 nan 0.1000 -0.0001
## 400 0.0342 nan 0.1000 -0.0001
## 420 0.0301 nan 0.1000 -0.0001
## 440 0.0266 nan 0.1000 -0.0001
## 460 0.0237 nan 0.1000 -0.0000
## 480 0.0208 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0375
## 2 1.1630 nan 0.1000 0.0329
## 3 1.1047 nan 0.1000 0.0271
## 4 1.0466 nan 0.1000 0.0279
## 5 0.9928 nan 0.1000 0.0218
## 6 0.9458 nan 0.1000 0.0192
## 7 0.9101 nan 0.1000 0.0136
## 8 0.8782 nan 0.1000 0.0107
## 9 0.8472 nan 0.1000 0.0114
## 10 0.8229 nan 0.1000 0.0097
## 20 0.6627 nan 0.1000 0.0015
## 40 0.5041 nan 0.1000 0.0004
## 60 0.4121 nan 0.1000 -0.0004
## 80 0.3435 nan 0.1000 -0.0011
## 100 0.2873 nan 0.1000 0.0001
## 120 0.2453 nan 0.1000 -0.0012
## 140 0.2119 nan 0.1000 -0.0004
## 160 0.1844 nan 0.1000 -0.0008
## 180 0.1581 nan 0.1000 -0.0003
## 200 0.1380 nan 0.1000 -0.0003
## 220 0.1193 nan 0.1000 -0.0003
## 240 0.1043 nan 0.1000 -0.0001
## 260 0.0901 nan 0.1000 -0.0001
## 280 0.0785 nan 0.1000 -0.0002
## 300 0.0692 nan 0.1000 -0.0002
## 320 0.0604 nan 0.1000 -0.0001
## 340 0.0527 nan 0.1000 -0.0001
## 360 0.0468 nan 0.1000 -0.0002
## 380 0.0410 nan 0.1000 -0.0001
## 400 0.0361 nan 0.1000 -0.0000
## 420 0.0319 nan 0.1000 -0.0001
## 440 0.0281 nan 0.1000 -0.0001
## 460 0.0249 nan 0.1000 -0.0000
## 480 0.0218 nan 0.1000 -0.0000
## 500 0.0191 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2309 nan 0.1000 0.0405
## 2 1.1600 nan 0.1000 0.0298
## 3 1.1009 nan 0.1000 0.0243
## 4 1.0436 nan 0.1000 0.0293
## 5 0.9930 nan 0.1000 0.0208
## 6 0.9514 nan 0.1000 0.0193
## 7 0.9212 nan 0.1000 0.0110
## 8 0.8878 nan 0.1000 0.0122
## 9 0.8601 nan 0.1000 0.0126
## 10 0.8376 nan 0.1000 0.0080
## 20 0.6740 nan 0.1000 0.0017
## 40 0.5316 nan 0.1000 0.0006
## 60 0.4325 nan 0.1000 -0.0009
## 80 0.3571 nan 0.1000 0.0005
## 100 0.3029 nan 0.1000 -0.0006
## 120 0.2578 nan 0.1000 -0.0004
## 140 0.2245 nan 0.1000 -0.0018
## 160 0.1924 nan 0.1000 -0.0005
## 180 0.1682 nan 0.1000 -0.0004
## 200 0.1489 nan 0.1000 -0.0005
## 220 0.1317 nan 0.1000 -0.0006
## 240 0.1138 nan 0.1000 -0.0007
## 260 0.0998 nan 0.1000 -0.0004
## 280 0.0886 nan 0.1000 -0.0001
## 300 0.0770 nan 0.1000 -0.0003
## 320 0.0677 nan 0.1000 -0.0002
## 340 0.0600 nan 0.1000 -0.0001
## 360 0.0529 nan 0.1000 -0.0001
## 380 0.0474 nan 0.1000 -0.0002
## 400 0.0422 nan 0.1000 -0.0002
## 420 0.0368 nan 0.1000 -0.0001
## 440 0.0325 nan 0.1000 -0.0001
## 460 0.0295 nan 0.1000 -0.0002
## 480 0.0259 nan 0.1000 -0.0002
## 500 0.0227 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0003
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2715 nan 0.0010 0.0004
## 80 1.2565 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2276 nan 0.0010 0.0003
## 140 1.2138 nan 0.0010 0.0003
## 160 1.2003 nan 0.0010 0.0003
## 180 1.1874 nan 0.0010 0.0003
## 200 1.1748 nan 0.0010 0.0002
## 220 1.1630 nan 0.0010 0.0003
## 240 1.1512 nan 0.0010 0.0002
## 260 1.1395 nan 0.0010 0.0003
## 280 1.1284 nan 0.0010 0.0002
## 300 1.1175 nan 0.0010 0.0003
## 320 1.1072 nan 0.0010 0.0002
## 340 1.0970 nan 0.0010 0.0002
## 360 1.0872 nan 0.0010 0.0002
## 380 1.0777 nan 0.0010 0.0002
## 400 1.0685 nan 0.0010 0.0002
## 420 1.0593 nan 0.0010 0.0002
## 440 1.0504 nan 0.0010 0.0002
## 460 1.0416 nan 0.0010 0.0002
## 480 1.0331 nan 0.0010 0.0002
## 500 1.0249 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2725 nan 0.0010 0.0004
## 80 1.2572 nan 0.0010 0.0004
## 100 1.2428 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2019 nan 0.0010 0.0003
## 180 1.1890 nan 0.0010 0.0003
## 200 1.1764 nan 0.0010 0.0003
## 220 1.1641 nan 0.0010 0.0003
## 240 1.1525 nan 0.0010 0.0003
## 260 1.1412 nan 0.0010 0.0002
## 280 1.1300 nan 0.0010 0.0002
## 300 1.1194 nan 0.0010 0.0002
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0985 nan 0.0010 0.0002
## 360 1.0885 nan 0.0010 0.0002
## 380 1.0789 nan 0.0010 0.0002
## 400 1.0699 nan 0.0010 0.0001
## 420 1.0607 nan 0.0010 0.0002
## 440 1.0518 nan 0.0010 0.0002
## 460 1.0432 nan 0.0010 0.0002
## 480 1.0348 nan 0.0010 0.0002
## 500 1.0266 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2879 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2575 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2151 nan 0.0010 0.0003
## 160 1.2021 nan 0.0010 0.0002
## 180 1.1893 nan 0.0010 0.0002
## 200 1.1768 nan 0.0010 0.0003
## 220 1.1645 nan 0.0010 0.0003
## 240 1.1530 nan 0.0010 0.0002
## 260 1.1415 nan 0.0010 0.0003
## 280 1.1305 nan 0.0010 0.0002
## 300 1.1199 nan 0.0010 0.0002
## 320 1.1096 nan 0.0010 0.0002
## 340 1.0995 nan 0.0010 0.0002
## 360 1.0898 nan 0.0010 0.0002
## 380 1.0803 nan 0.0010 0.0002
## 400 1.0710 nan 0.0010 0.0002
## 420 1.0620 nan 0.0010 0.0002
## 440 1.0533 nan 0.0010 0.0002
## 460 1.0448 nan 0.0010 0.0002
## 480 1.0363 nan 0.0010 0.0002
## 500 1.0281 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2218 nan 0.0010 0.0003
## 140 1.2070 nan 0.0010 0.0003
## 160 1.1927 nan 0.0010 0.0003
## 180 1.1794 nan 0.0010 0.0003
## 200 1.1660 nan 0.0010 0.0003
## 220 1.1529 nan 0.0010 0.0003
## 240 1.1406 nan 0.0010 0.0003
## 260 1.1286 nan 0.0010 0.0002
## 280 1.1169 nan 0.0010 0.0003
## 300 1.1055 nan 0.0010 0.0002
## 320 1.0943 nan 0.0010 0.0003
## 340 1.0837 nan 0.0010 0.0002
## 360 1.0734 nan 0.0010 0.0002
## 380 1.0632 nan 0.0010 0.0002
## 400 1.0532 nan 0.0010 0.0002
## 420 1.0438 nan 0.0010 0.0002
## 440 1.0344 nan 0.0010 0.0002
## 460 1.0252 nan 0.0010 0.0002
## 480 1.0166 nan 0.0010 0.0002
## 500 1.0080 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0003
## 80 1.2533 nan 0.0010 0.0003
## 100 1.2379 nan 0.0010 0.0003
## 120 1.2230 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0003
## 160 1.1948 nan 0.0010 0.0003
## 180 1.1811 nan 0.0010 0.0003
## 200 1.1680 nan 0.0010 0.0002
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1430 nan 0.0010 0.0003
## 260 1.1311 nan 0.0010 0.0003
## 280 1.1193 nan 0.0010 0.0003
## 300 1.1079 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0863 nan 0.0010 0.0002
## 360 1.0756 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0554 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
## 440 1.0365 nan 0.0010 0.0002
## 460 1.0276 nan 0.0010 0.0002
## 480 1.0190 nan 0.0010 0.0002
## 500 1.0103 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2394 nan 0.0010 0.0003
## 120 1.2250 nan 0.0010 0.0003
## 140 1.2107 nan 0.0010 0.0003
## 160 1.1969 nan 0.0010 0.0003
## 180 1.1834 nan 0.0010 0.0003
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1578 nan 0.0010 0.0003
## 240 1.1455 nan 0.0010 0.0003
## 260 1.1338 nan 0.0010 0.0003
## 280 1.1219 nan 0.0010 0.0003
## 300 1.1106 nan 0.0010 0.0002
## 320 1.0998 nan 0.0010 0.0002
## 340 1.0893 nan 0.0010 0.0003
## 360 1.0789 nan 0.0010 0.0002
## 380 1.0689 nan 0.0010 0.0002
## 400 1.0592 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0002
## 440 1.0403 nan 0.0010 0.0002
## 460 1.0313 nan 0.0010 0.0002
## 480 1.0225 nan 0.0010 0.0002
## 500 1.0140 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0003
## 80 1.2497 nan 0.0010 0.0003
## 100 1.2336 nan 0.0010 0.0004
## 120 1.2181 nan 0.0010 0.0003
## 140 1.2029 nan 0.0010 0.0003
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1738 nan 0.0010 0.0003
## 200 1.1598 nan 0.0010 0.0003
## 220 1.1467 nan 0.0010 0.0003
## 240 1.1337 nan 0.0010 0.0003
## 260 1.1214 nan 0.0010 0.0003
## 280 1.1088 nan 0.0010 0.0003
## 300 1.0968 nan 0.0010 0.0003
## 320 1.0850 nan 0.0010 0.0003
## 340 1.0739 nan 0.0010 0.0002
## 360 1.0630 nan 0.0010 0.0002
## 380 1.0524 nan 0.0010 0.0002
## 400 1.0421 nan 0.0010 0.0002
## 420 1.0319 nan 0.0010 0.0002
## 440 1.0221 nan 0.0010 0.0002
## 460 1.0127 nan 0.0010 0.0002
## 480 1.0037 nan 0.0010 0.0002
## 500 0.9948 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2665 nan 0.0010 0.0004
## 80 1.2501 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0004
## 120 1.2185 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1886 nan 0.0010 0.0003
## 180 1.1749 nan 0.0010 0.0003
## 200 1.1611 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1225 nan 0.0010 0.0003
## 280 1.1105 nan 0.0010 0.0003
## 300 1.0988 nan 0.0010 0.0002
## 320 1.0874 nan 0.0010 0.0003
## 340 1.0766 nan 0.0010 0.0002
## 360 1.0658 nan 0.0010 0.0002
## 380 1.0551 nan 0.0010 0.0002
## 400 1.0450 nan 0.0010 0.0002
## 420 1.0350 nan 0.0010 0.0002
## 440 1.0255 nan 0.0010 0.0002
## 460 1.0161 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
## 500 0.9978 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2360 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0003
## 140 1.2055 nan 0.0010 0.0003
## 160 1.1909 nan 0.0010 0.0003
## 180 1.1774 nan 0.0010 0.0003
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## 220 1.1509 nan 0.0010 0.0003
## 240 1.1384 nan 0.0010 0.0003
## 260 1.1261 nan 0.0010 0.0002
## 280 1.1142 nan 0.0010 0.0003
## 300 1.1025 nan 0.0010 0.0002
## 320 1.0911 nan 0.0010 0.0002
## 340 1.0802 nan 0.0010 0.0002
## 360 1.0696 nan 0.0010 0.0002
## 380 1.0593 nan 0.0010 0.0002
## 400 1.0494 nan 0.0010 0.0002
## 420 1.0398 nan 0.0010 0.0002
## 440 1.0303 nan 0.0010 0.0002
## 460 1.0210 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0035 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0038
## 2 1.3049 nan 0.0100 0.0037
## 3 1.2969 nan 0.0100 0.0039
## 4 1.2888 nan 0.0100 0.0034
## 5 1.2806 nan 0.0100 0.0037
## 6 1.2719 nan 0.0100 0.0041
## 7 1.2643 nan 0.0100 0.0032
## 8 1.2569 nan 0.0100 0.0033
## 9 1.2502 nan 0.0100 0.0032
## 10 1.2433 nan 0.0100 0.0030
## 20 1.1760 nan 0.0100 0.0025
## 40 1.0683 nan 0.0100 0.0021
## 60 0.9871 nan 0.0100 0.0015
## 80 0.9241 nan 0.0100 0.0009
## 100 0.8714 nan 0.0100 0.0009
## 120 0.8295 nan 0.0100 0.0006
## 140 0.7950 nan 0.0100 0.0003
## 160 0.7657 nan 0.0100 0.0004
## 180 0.7411 nan 0.0100 0.0004
## 200 0.7176 nan 0.0100 0.0001
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## 260 0.6652 nan 0.0100 0.0001
## 280 0.6519 nan 0.0100 0.0001
## 300 0.6376 nan 0.0100 -0.0000
## 320 0.6249 nan 0.0100 -0.0001
## 340 0.6132 nan 0.0100 0.0001
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## 380 0.5918 nan 0.0100 0.0001
## 400 0.5818 nan 0.0100 -0.0000
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## 460 0.5545 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3037 nan 0.0100 0.0038
## 3 1.2952 nan 0.0100 0.0038
## 4 1.2870 nan 0.0100 0.0039
## 5 1.2789 nan 0.0100 0.0039
## 6 1.2712 nan 0.0100 0.0039
## 7 1.2637 nan 0.0100 0.0031
## 8 1.2563 nan 0.0100 0.0034
## 9 1.2493 nan 0.0100 0.0031
## 10 1.2419 nan 0.0100 0.0032
## 20 1.1761 nan 0.0100 0.0027
## 40 1.0692 nan 0.0100 0.0020
## 60 0.9864 nan 0.0100 0.0014
## 80 0.9236 nan 0.0100 0.0012
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## 320 0.6336 nan 0.0100 -0.0000
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## 380 0.6010 nan 0.0100 -0.0001
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## 460 0.5628 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0035
## 2 1.3047 nan 0.0100 0.0039
## 3 1.2964 nan 0.0100 0.0039
## 4 1.2887 nan 0.0100 0.0033
## 5 1.2816 nan 0.0100 0.0033
## 6 1.2735 nan 0.0100 0.0037
## 7 1.2659 nan 0.0100 0.0036
## 8 1.2592 nan 0.0100 0.0033
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## 40 1.0736 nan 0.0100 0.0021
## 60 0.9917 nan 0.0100 0.0015
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## 360 0.6151 nan 0.0100 -0.0000
## 380 0.6051 nan 0.0100 -0.0001
## 400 0.5951 nan 0.0100 -0.0001
## 420 0.5855 nan 0.0100 0.0001
## 440 0.5769 nan 0.0100 0.0001
## 460 0.5684 nan 0.0100 -0.0001
## 480 0.5609 nan 0.0100 0.0000
## 500 0.5523 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2941 nan 0.0100 0.0035
## 4 1.2853 nan 0.0100 0.0042
## 5 1.2770 nan 0.0100 0.0035
## 6 1.2697 nan 0.0100 0.0034
## 7 1.2615 nan 0.0100 0.0037
## 8 1.2535 nan 0.0100 0.0037
## 9 1.2458 nan 0.0100 0.0036
## 10 1.2381 nan 0.0100 0.0034
## 20 1.1665 nan 0.0100 0.0030
## 40 1.0527 nan 0.0100 0.0021
## 60 0.9678 nan 0.0100 0.0013
## 80 0.9002 nan 0.0100 0.0010
## 100 0.8461 nan 0.0100 0.0008
## 120 0.8025 nan 0.0100 0.0003
## 140 0.7666 nan 0.0100 0.0004
## 160 0.7349 nan 0.0100 0.0005
## 180 0.7082 nan 0.0100 0.0001
## 200 0.6841 nan 0.0100 0.0001
## 220 0.6632 nan 0.0100 0.0003
## 240 0.6453 nan 0.0100 0.0002
## 260 0.6281 nan 0.0100 0.0002
## 280 0.6111 nan 0.0100 0.0001
## 300 0.5970 nan 0.0100 0.0001
## 320 0.5831 nan 0.0100 0.0001
## 340 0.5706 nan 0.0100 0.0001
## 360 0.5586 nan 0.0100 0.0001
## 380 0.5471 nan 0.0100 -0.0000
## 400 0.5364 nan 0.0100 -0.0001
## 420 0.5270 nan 0.0100 0.0000
## 440 0.5167 nan 0.0100 0.0000
## 460 0.5068 nan 0.0100 -0.0000
## 480 0.4976 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3032 nan 0.0100 0.0038
## 3 1.2941 nan 0.0100 0.0042
## 4 1.2859 nan 0.0100 0.0036
## 5 1.2774 nan 0.0100 0.0040
## 6 1.2690 nan 0.0100 0.0036
## 7 1.2602 nan 0.0100 0.0040
## 8 1.2519 nan 0.0100 0.0040
## 9 1.2439 nan 0.0100 0.0035
## 10 1.2361 nan 0.0100 0.0030
## 20 1.1680 nan 0.0100 0.0024
## 40 1.0547 nan 0.0100 0.0022
## 60 0.9700 nan 0.0100 0.0015
## 80 0.9026 nan 0.0100 0.0012
## 100 0.8493 nan 0.0100 0.0009
## 120 0.8046 nan 0.0100 0.0007
## 140 0.7685 nan 0.0100 0.0006
## 160 0.7377 nan 0.0100 0.0004
## 180 0.7110 nan 0.0100 0.0003
## 200 0.6885 nan 0.0100 0.0004
## 220 0.6672 nan 0.0100 0.0000
## 240 0.6494 nan 0.0100 0.0002
## 260 0.6330 nan 0.0100 0.0000
## 280 0.6179 nan 0.0100 -0.0001
## 300 0.6055 nan 0.0100 0.0001
## 320 0.5928 nan 0.0100 0.0000
## 340 0.5796 nan 0.0100 0.0000
## 360 0.5671 nan 0.0100 -0.0000
## 380 0.5564 nan 0.0100 0.0001
## 400 0.5461 nan 0.0100 -0.0000
## 420 0.5366 nan 0.0100 -0.0002
## 440 0.5263 nan 0.0100 0.0000
## 460 0.5170 nan 0.0100 0.0001
## 480 0.5075 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3026 nan 0.0100 0.0039
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2856 nan 0.0100 0.0042
## 5 1.2772 nan 0.0100 0.0040
## 6 1.2689 nan 0.0100 0.0041
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2531 nan 0.0100 0.0036
## 9 1.2455 nan 0.0100 0.0032
## 10 1.2381 nan 0.0100 0.0035
## 20 1.1700 nan 0.0100 0.0029
## 40 1.0587 nan 0.0100 0.0019
## 60 0.9729 nan 0.0100 0.0013
## 80 0.9059 nan 0.0100 0.0013
## 100 0.8523 nan 0.0100 0.0006
## 120 0.8083 nan 0.0100 0.0004
## 140 0.7724 nan 0.0100 0.0007
## 160 0.7412 nan 0.0100 0.0005
## 180 0.7152 nan 0.0100 -0.0001
## 200 0.6921 nan 0.0100 0.0002
## 220 0.6712 nan 0.0100 0.0003
## 240 0.6530 nan 0.0100 -0.0001
## 260 0.6378 nan 0.0100 0.0001
## 280 0.6226 nan 0.0100 -0.0000
## 300 0.6095 nan 0.0100 0.0001
## 320 0.5971 nan 0.0100 -0.0002
## 340 0.5830 nan 0.0100 -0.0000
## 360 0.5706 nan 0.0100 -0.0001
## 380 0.5603 nan 0.0100 -0.0000
## 400 0.5489 nan 0.0100 0.0001
## 420 0.5386 nan 0.0100 -0.0001
## 440 0.5291 nan 0.0100 0.0000
## 460 0.5201 nan 0.0100 0.0000
## 480 0.5113 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0041
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2927 nan 0.0100 0.0042
## 4 1.2842 nan 0.0100 0.0041
## 5 1.2762 nan 0.0100 0.0034
## 6 1.2672 nan 0.0100 0.0039
## 7 1.2587 nan 0.0100 0.0040
## 8 1.2504 nan 0.0100 0.0038
## 9 1.2425 nan 0.0100 0.0036
## 10 1.2339 nan 0.0100 0.0037
## 20 1.1602 nan 0.0100 0.0028
## 40 1.0430 nan 0.0100 0.0020
## 60 0.9540 nan 0.0100 0.0016
## 80 0.8839 nan 0.0100 0.0010
## 100 0.8287 nan 0.0100 0.0009
## 120 0.7829 nan 0.0100 0.0006
## 140 0.7430 nan 0.0100 0.0005
## 160 0.7100 nan 0.0100 0.0003
## 180 0.6816 nan 0.0100 0.0002
## 200 0.6566 nan 0.0100 0.0001
## 220 0.6339 nan 0.0100 0.0000
## 240 0.6130 nan 0.0100 0.0000
## 260 0.5939 nan 0.0100 0.0000
## 280 0.5769 nan 0.0100 0.0001
## 300 0.5621 nan 0.0100 0.0000
## 320 0.5483 nan 0.0100 -0.0000
## 340 0.5333 nan 0.0100 0.0001
## 360 0.5201 nan 0.0100 -0.0000
## 380 0.5073 nan 0.0100 0.0000
## 400 0.4955 nan 0.0100 0.0001
## 420 0.4844 nan 0.0100 0.0000
## 440 0.4740 nan 0.0100 0.0000
## 460 0.4641 nan 0.0100 0.0001
## 480 0.4543 nan 0.0100 -0.0003
## 500 0.4447 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0042
## 2 1.3015 nan 0.0100 0.0046
## 3 1.2923 nan 0.0100 0.0042
## 4 1.2832 nan 0.0100 0.0042
## 5 1.2742 nan 0.0100 0.0038
## 6 1.2660 nan 0.0100 0.0040
## 7 1.2578 nan 0.0100 0.0037
## 8 1.2498 nan 0.0100 0.0037
## 9 1.2414 nan 0.0100 0.0040
## 10 1.2337 nan 0.0100 0.0036
## 20 1.1602 nan 0.0100 0.0030
## 40 1.0442 nan 0.0100 0.0018
## 60 0.9556 nan 0.0100 0.0017
## 80 0.8865 nan 0.0100 0.0013
## 100 0.8326 nan 0.0100 0.0008
## 120 0.7860 nan 0.0100 0.0007
## 140 0.7476 nan 0.0100 0.0006
## 160 0.7151 nan 0.0100 0.0004
## 180 0.6870 nan 0.0100 0.0003
## 200 0.6629 nan 0.0100 0.0000
## 220 0.6408 nan 0.0100 0.0002
## 240 0.6206 nan 0.0100 0.0001
## 260 0.6024 nan 0.0100 0.0001
## 280 0.5843 nan 0.0100 0.0002
## 300 0.5680 nan 0.0100 0.0001
## 320 0.5538 nan 0.0100 0.0000
## 340 0.5392 nan 0.0100 -0.0000
## 360 0.5260 nan 0.0100 0.0001
## 380 0.5148 nan 0.0100 0.0001
## 400 0.5018 nan 0.0100 0.0000
## 420 0.4902 nan 0.0100 0.0000
## 440 0.4786 nan 0.0100 -0.0001
## 460 0.4682 nan 0.0100 -0.0001
## 480 0.4580 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0039
## 3 1.2936 nan 0.0100 0.0035
## 4 1.2849 nan 0.0100 0.0042
## 5 1.2759 nan 0.0100 0.0038
## 6 1.2665 nan 0.0100 0.0044
## 7 1.2585 nan 0.0100 0.0037
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2419 nan 0.0100 0.0039
## 10 1.2341 nan 0.0100 0.0038
## 20 1.1620 nan 0.0100 0.0027
## 40 1.0472 nan 0.0100 0.0021
## 60 0.9606 nan 0.0100 0.0017
## 80 0.8927 nan 0.0100 0.0013
## 100 0.8378 nan 0.0100 0.0009
## 120 0.7948 nan 0.0100 0.0007
## 140 0.7575 nan 0.0100 0.0006
## 160 0.7250 nan 0.0100 0.0003
## 180 0.6976 nan 0.0100 0.0002
## 200 0.6729 nan 0.0100 0.0003
## 220 0.6503 nan 0.0100 0.0002
## 240 0.6305 nan 0.0100 0.0001
## 260 0.6126 nan 0.0100 0.0000
## 280 0.5961 nan 0.0100 0.0001
## 300 0.5808 nan 0.0100 0.0001
## 320 0.5664 nan 0.0100 0.0001
## 340 0.5521 nan 0.0100 0.0001
## 360 0.5389 nan 0.0100 -0.0000
## 380 0.5266 nan 0.0100 0.0001
## 400 0.5143 nan 0.0100 0.0002
## 420 0.5034 nan 0.0100 -0.0001
## 440 0.4923 nan 0.0100 0.0002
## 460 0.4825 nan 0.0100 -0.0001
## 480 0.4727 nan 0.0100 -0.0001
## 500 0.4638 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2483 nan 0.1000 0.0306
## 2 1.1826 nan 0.1000 0.0268
## 3 1.1189 nan 0.1000 0.0292
## 4 1.0711 nan 0.1000 0.0230
## 5 1.0335 nan 0.1000 0.0149
## 6 0.9966 nan 0.1000 0.0168
## 7 0.9630 nan 0.1000 0.0138
## 8 0.9302 nan 0.1000 0.0130
## 9 0.8994 nan 0.1000 0.0111
## 10 0.8727 nan 0.1000 0.0105
## 20 0.7253 nan 0.1000 0.0006
## 40 0.5918 nan 0.1000 -0.0014
## 60 0.5021 nan 0.1000 -0.0016
## 80 0.4391 nan 0.1000 -0.0009
## 100 0.3877 nan 0.1000 -0.0002
## 120 0.3458 nan 0.1000 -0.0008
## 140 0.3072 nan 0.1000 -0.0005
## 160 0.2721 nan 0.1000 -0.0004
## 180 0.2441 nan 0.1000 -0.0001
## 200 0.2215 nan 0.1000 -0.0002
## 220 0.1999 nan 0.1000 0.0001
## 240 0.1821 nan 0.1000 -0.0001
## 260 0.1663 nan 0.1000 -0.0003
## 280 0.1533 nan 0.1000 -0.0005
## 300 0.1399 nan 0.1000 0.0001
## 320 0.1278 nan 0.1000 -0.0004
## 340 0.1169 nan 0.1000 -0.0001
## 360 0.1067 nan 0.1000 -0.0000
## 380 0.0985 nan 0.1000 -0.0001
## 400 0.0909 nan 0.1000 0.0000
## 420 0.0836 nan 0.1000 0.0001
## 440 0.0775 nan 0.1000 -0.0003
## 460 0.0708 nan 0.1000 -0.0001
## 480 0.0654 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2383 nan 0.1000 0.0402
## 2 1.1679 nan 0.1000 0.0306
## 3 1.1096 nan 0.1000 0.0246
## 4 1.0571 nan 0.1000 0.0252
## 5 1.0135 nan 0.1000 0.0163
## 6 0.9796 nan 0.1000 0.0145
## 7 0.9468 nan 0.1000 0.0131
## 8 0.9171 nan 0.1000 0.0122
## 9 0.8901 nan 0.1000 0.0116
## 10 0.8697 nan 0.1000 0.0075
## 20 0.7178 nan 0.1000 0.0022
## 40 0.5903 nan 0.1000 0.0004
## 60 0.5104 nan 0.1000 -0.0007
## 80 0.4429 nan 0.1000 0.0004
## 100 0.3955 nan 0.1000 -0.0011
## 120 0.3543 nan 0.1000 -0.0013
## 140 0.3172 nan 0.1000 0.0004
## 160 0.2857 nan 0.1000 -0.0011
## 180 0.2617 nan 0.1000 -0.0004
## 200 0.2372 nan 0.1000 0.0002
## 220 0.2126 nan 0.1000 -0.0003
## 240 0.1948 nan 0.1000 -0.0007
## 260 0.1785 nan 0.1000 -0.0009
## 280 0.1622 nan 0.1000 -0.0006
## 300 0.1493 nan 0.1000 -0.0004
## 320 0.1396 nan 0.1000 -0.0003
## 340 0.1279 nan 0.1000 -0.0000
## 360 0.1176 nan 0.1000 -0.0003
## 380 0.1090 nan 0.1000 -0.0006
## 400 0.1000 nan 0.1000 -0.0002
## 420 0.0930 nan 0.1000 -0.0002
## 440 0.0855 nan 0.1000 -0.0002
## 460 0.0784 nan 0.1000 -0.0002
## 480 0.0731 nan 0.1000 -0.0003
## 500 0.0670 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0386
## 2 1.1708 nan 0.1000 0.0287
## 3 1.1129 nan 0.1000 0.0265
## 4 1.0596 nan 0.1000 0.0223
## 5 1.0126 nan 0.1000 0.0213
## 6 0.9773 nan 0.1000 0.0144
## 7 0.9474 nan 0.1000 0.0112
## 8 0.9179 nan 0.1000 0.0127
## 9 0.8930 nan 0.1000 0.0106
## 10 0.8675 nan 0.1000 0.0087
## 20 0.7225 nan 0.1000 0.0021
## 40 0.5959 nan 0.1000 -0.0010
## 60 0.5162 nan 0.1000 -0.0005
## 80 0.4660 nan 0.1000 -0.0007
## 100 0.4098 nan 0.1000 -0.0012
## 120 0.3703 nan 0.1000 -0.0001
## 140 0.3327 nan 0.1000 -0.0010
## 160 0.3024 nan 0.1000 -0.0004
## 180 0.2746 nan 0.1000 -0.0008
## 200 0.2473 nan 0.1000 -0.0001
## 220 0.2245 nan 0.1000 -0.0006
## 240 0.2046 nan 0.1000 -0.0011
## 260 0.1849 nan 0.1000 -0.0004
## 280 0.1696 nan 0.1000 -0.0006
## 300 0.1545 nan 0.1000 -0.0004
## 320 0.1429 nan 0.1000 -0.0005
## 340 0.1324 nan 0.1000 -0.0006
## 360 0.1224 nan 0.1000 -0.0002
## 380 0.1129 nan 0.1000 -0.0004
## 400 0.1044 nan 0.1000 -0.0004
## 420 0.0976 nan 0.1000 -0.0003
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## 460 0.0839 nan 0.1000 -0.0003
## 480 0.0783 nan 0.1000 -0.0002
## 500 0.0724 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2361 nan 0.1000 0.0385
## 2 1.1581 nan 0.1000 0.0322
## 3 1.0994 nan 0.1000 0.0239
## 4 1.0521 nan 0.1000 0.0210
## 5 1.0070 nan 0.1000 0.0177
## 6 0.9646 nan 0.1000 0.0179
## 7 0.9290 nan 0.1000 0.0133
## 8 0.8963 nan 0.1000 0.0125
## 9 0.8661 nan 0.1000 0.0111
## 10 0.8413 nan 0.1000 0.0102
## 20 0.6926 nan 0.1000 0.0006
## 40 0.5433 nan 0.1000 0.0003
## 60 0.4517 nan 0.1000 0.0007
## 80 0.3828 nan 0.1000 -0.0006
## 100 0.3256 nan 0.1000 -0.0010
## 120 0.2820 nan 0.1000 -0.0007
## 140 0.2463 nan 0.1000 -0.0003
## 160 0.2157 nan 0.1000 -0.0003
## 180 0.1909 nan 0.1000 -0.0006
## 200 0.1690 nan 0.1000 0.0001
## 220 0.1505 nan 0.1000 -0.0004
## 240 0.1343 nan 0.1000 0.0004
## 260 0.1184 nan 0.1000 -0.0004
## 280 0.1058 nan 0.1000 -0.0003
## 300 0.0957 nan 0.1000 -0.0003
## 320 0.0861 nan 0.1000 -0.0001
## 340 0.0776 nan 0.1000 -0.0001
## 360 0.0702 nan 0.1000 -0.0004
## 380 0.0635 nan 0.1000 0.0000
## 400 0.0576 nan 0.1000 -0.0001
## 420 0.0523 nan 0.1000 -0.0001
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## 460 0.0435 nan 0.1000 -0.0001
## 480 0.0394 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0357
## 2 1.1730 nan 0.1000 0.0261
## 3 1.1129 nan 0.1000 0.0283
## 4 1.0593 nan 0.1000 0.0264
## 5 1.0148 nan 0.1000 0.0194
## 6 0.9754 nan 0.1000 0.0163
## 7 0.9439 nan 0.1000 0.0129
## 8 0.9099 nan 0.1000 0.0139
## 9 0.8850 nan 0.1000 0.0089
## 10 0.8606 nan 0.1000 0.0100
## 20 0.6933 nan 0.1000 0.0024
## 40 0.5384 nan 0.1000 -0.0003
## 60 0.4508 nan 0.1000 -0.0010
## 80 0.3819 nan 0.1000 -0.0000
## 100 0.3260 nan 0.1000 -0.0016
## 120 0.2834 nan 0.1000 -0.0007
## 140 0.2492 nan 0.1000 -0.0014
## 160 0.2154 nan 0.1000 -0.0007
## 180 0.1901 nan 0.1000 -0.0003
## 200 0.1668 nan 0.1000 -0.0004
## 220 0.1491 nan 0.1000 -0.0006
## 240 0.1331 nan 0.1000 -0.0006
## 260 0.1190 nan 0.1000 -0.0001
## 280 0.1059 nan 0.1000 -0.0004
## 300 0.0932 nan 0.1000 -0.0000
## 320 0.0846 nan 0.1000 -0.0006
## 340 0.0757 nan 0.1000 -0.0003
## 360 0.0688 nan 0.1000 -0.0002
## 380 0.0624 nan 0.1000 -0.0001
## 400 0.0559 nan 0.1000 0.0000
## 420 0.0509 nan 0.1000 -0.0003
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## 460 0.0418 nan 0.1000 -0.0002
## 480 0.0382 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0364
## 2 1.1674 nan 0.1000 0.0301
## 3 1.1078 nan 0.1000 0.0268
## 4 1.0594 nan 0.1000 0.0184
## 5 1.0101 nan 0.1000 0.0223
## 6 0.9692 nan 0.1000 0.0150
## 7 0.9322 nan 0.1000 0.0150
## 8 0.9005 nan 0.1000 0.0105
## 9 0.8757 nan 0.1000 0.0092
## 10 0.8509 nan 0.1000 0.0104
## 20 0.6978 nan 0.1000 0.0037
## 40 0.5521 nan 0.1000 -0.0003
## 60 0.4612 nan 0.1000 0.0006
## 80 0.4010 nan 0.1000 -0.0009
## 100 0.3486 nan 0.1000 -0.0014
## 120 0.3023 nan 0.1000 -0.0005
## 140 0.2687 nan 0.1000 -0.0014
## 160 0.2411 nan 0.1000 -0.0008
## 180 0.2162 nan 0.1000 -0.0004
## 200 0.1938 nan 0.1000 -0.0006
## 220 0.1769 nan 0.1000 -0.0004
## 240 0.1591 nan 0.1000 -0.0009
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## 280 0.1274 nan 0.1000 -0.0007
## 300 0.1145 nan 0.1000 -0.0005
## 320 0.1045 nan 0.1000 -0.0003
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## 480 0.0490 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0392
## 2 1.1556 nan 0.1000 0.0354
## 3 1.0920 nan 0.1000 0.0266
## 4 1.0376 nan 0.1000 0.0239
## 5 0.9931 nan 0.1000 0.0164
## 6 0.9529 nan 0.1000 0.0104
## 7 0.9194 nan 0.1000 0.0121
## 8 0.8903 nan 0.1000 0.0110
## 9 0.8580 nan 0.1000 0.0143
## 10 0.8321 nan 0.1000 0.0107
## 20 0.6625 nan 0.1000 0.0012
## 40 0.5012 nan 0.1000 -0.0009
## 60 0.4067 nan 0.1000 -0.0002
## 80 0.3337 nan 0.1000 -0.0004
## 100 0.2755 nan 0.1000 -0.0014
## 120 0.2341 nan 0.1000 0.0009
## 140 0.2010 nan 0.1000 -0.0002
## 160 0.1710 nan 0.1000 -0.0005
## 180 0.1482 nan 0.1000 -0.0006
## 200 0.1288 nan 0.1000 -0.0004
## 220 0.1120 nan 0.1000 -0.0005
## 240 0.0982 nan 0.1000 -0.0001
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## 280 0.0767 nan 0.1000 -0.0004
## 300 0.0684 nan 0.1000 -0.0003
## 320 0.0605 nan 0.1000 -0.0001
## 340 0.0531 nan 0.1000 -0.0001
## 360 0.0470 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2288 nan 0.1000 0.0399
## 2 1.1572 nan 0.1000 0.0328
## 3 1.0878 nan 0.1000 0.0307
## 4 1.0359 nan 0.1000 0.0214
## 5 0.9871 nan 0.1000 0.0211
## 6 0.9441 nan 0.1000 0.0177
## 7 0.9103 nan 0.1000 0.0140
## 8 0.8803 nan 0.1000 0.0092
## 9 0.8450 nan 0.1000 0.0125
## 10 0.8216 nan 0.1000 0.0089
## 20 0.6622 nan 0.1000 0.0015
## 40 0.5085 nan 0.1000 0.0006
## 60 0.4171 nan 0.1000 -0.0011
## 80 0.3406 nan 0.1000 -0.0019
## 100 0.2853 nan 0.1000 -0.0006
## 120 0.2429 nan 0.1000 -0.0009
## 140 0.2099 nan 0.1000 -0.0008
## 160 0.1835 nan 0.1000 0.0002
## 180 0.1570 nan 0.1000 0.0000
## 200 0.1355 nan 0.1000 -0.0004
## 220 0.1181 nan 0.1000 -0.0005
## 240 0.1036 nan 0.1000 0.0001
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## 280 0.0782 nan 0.1000 -0.0003
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## 320 0.0615 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2368 nan 0.1000 0.0403
## 2 1.1601 nan 0.1000 0.0285
## 3 1.1013 nan 0.1000 0.0274
## 4 1.0415 nan 0.1000 0.0241
## 5 0.9961 nan 0.1000 0.0159
## 6 0.9587 nan 0.1000 0.0161
## 7 0.9198 nan 0.1000 0.0137
## 8 0.8826 nan 0.1000 0.0143
## 9 0.8552 nan 0.1000 0.0089
## 10 0.8311 nan 0.1000 0.0083
## 20 0.6761 nan 0.1000 0.0034
## 40 0.5186 nan 0.1000 0.0008
## 60 0.4242 nan 0.1000 -0.0013
## 80 0.3561 nan 0.1000 -0.0019
## 100 0.3020 nan 0.1000 -0.0014
## 120 0.2617 nan 0.1000 -0.0012
## 140 0.2262 nan 0.1000 -0.0005
## 160 0.1930 nan 0.1000 -0.0011
## 180 0.1657 nan 0.1000 -0.0007
## 200 0.1460 nan 0.1000 -0.0003
## 220 0.1272 nan 0.1000 -0.0004
## 240 0.1115 nan 0.1000 -0.0008
## 260 0.0983 nan 0.1000 -0.0003
## 280 0.0867 nan 0.1000 -0.0001
## 300 0.0783 nan 0.1000 -0.0005
## 320 0.0691 nan 0.1000 -0.0001
## 340 0.0622 nan 0.1000 -0.0002
## 360 0.0544 nan 0.1000 -0.0001
## 380 0.0482 nan 0.1000 -0.0002
## 400 0.0426 nan 0.1000 -0.0002
## 420 0.0379 nan 0.1000 -0.0001
## 440 0.0336 nan 0.1000 -0.0001
## 460 0.0302 nan 0.1000 -0.0002
## 480 0.0268 nan 0.1000 -0.0002
## 500 0.0241 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0003
## 40 1.2885 nan 0.0010 0.0004
## 60 1.2730 nan 0.0010 0.0004
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2299 nan 0.0010 0.0003
## 140 1.2168 nan 0.0010 0.0003
## 160 1.2041 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0003
## 200 1.1793 nan 0.0010 0.0003
## 220 1.1675 nan 0.0010 0.0002
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## 320 1.1137 nan 0.0010 0.0002
## 340 1.1034 nan 0.0010 0.0002
## 360 1.0939 nan 0.0010 0.0002
## 380 1.0847 nan 0.0010 0.0002
## 400 1.0755 nan 0.0010 0.0002
## 420 1.0665 nan 0.0010 0.0002
## 440 1.0580 nan 0.0010 0.0002
## 460 1.0494 nan 0.0010 0.0002
## 480 1.0413 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2889 nan 0.0010 0.0004
## 60 1.2737 nan 0.0010 0.0004
## 80 1.2589 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0004
## 120 1.2311 nan 0.0010 0.0003
## 140 1.2179 nan 0.0010 0.0002
## 160 1.2048 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0002
## 200 1.1804 nan 0.0010 0.0003
## 220 1.1688 nan 0.0010 0.0003
## 240 1.1572 nan 0.0010 0.0002
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## 280 1.1356 nan 0.0010 0.0002
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## 340 1.1047 nan 0.0010 0.0002
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## 380 1.0855 nan 0.0010 0.0002
## 400 1.0763 nan 0.0010 0.0002
## 420 1.0672 nan 0.0010 0.0002
## 440 1.0587 nan 0.0010 0.0002
## 460 1.0503 nan 0.0010 0.0002
## 480 1.0419 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0003
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2887 nan 0.0010 0.0003
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2590 nan 0.0010 0.0003
## 100 1.2451 nan 0.0010 0.0003
## 120 1.2313 nan 0.0010 0.0003
## 140 1.2179 nan 0.0010 0.0003
## 160 1.2049 nan 0.0010 0.0003
## 180 1.1925 nan 0.0010 0.0003
## 200 1.1802 nan 0.0010 0.0003
## 220 1.1683 nan 0.0010 0.0003
## 240 1.1571 nan 0.0010 0.0003
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## 280 1.1353 nan 0.0010 0.0002
## 300 1.1248 nan 0.0010 0.0002
## 320 1.1147 nan 0.0010 0.0002
## 340 1.1048 nan 0.0010 0.0002
## 360 1.0951 nan 0.0010 0.0002
## 380 1.0859 nan 0.0010 0.0002
## 400 1.0768 nan 0.0010 0.0002
## 420 1.0680 nan 0.0010 0.0002
## 440 1.0592 nan 0.0010 0.0002
## 460 1.0510 nan 0.0010 0.0002
## 480 1.0428 nan 0.0010 0.0002
## 500 1.0348 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0003
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0003
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2237 nan 0.0010 0.0004
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1956 nan 0.0010 0.0003
## 180 1.1822 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1446 nan 0.0010 0.0002
## 260 1.1327 nan 0.0010 0.0003
## 280 1.1211 nan 0.0010 0.0002
## 300 1.1096 nan 0.0010 0.0003
## 320 1.0988 nan 0.0010 0.0002
## 340 1.0884 nan 0.0010 0.0002
## 360 1.0781 nan 0.0010 0.0002
## 380 1.0684 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0493 nan 0.0010 0.0002
## 440 1.0400 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0223 nan 0.0010 0.0002
## 500 1.0138 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0003
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0003
## 100 1.2399 nan 0.0010 0.0003
## 120 1.2252 nan 0.0010 0.0003
## 140 1.2112 nan 0.0010 0.0003
## 160 1.1973 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1712 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0003
## 240 1.1465 nan 0.0010 0.0003
## 260 1.1346 nan 0.0010 0.0003
## 280 1.1229 nan 0.0010 0.0003
## 300 1.1116 nan 0.0010 0.0002
## 320 1.1008 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
## 360 1.0797 nan 0.0010 0.0002
## 380 1.0697 nan 0.0010 0.0003
## 400 1.0601 nan 0.0010 0.0002
## 420 1.0506 nan 0.0010 0.0002
## 440 1.0410 nan 0.0010 0.0002
## 460 1.0319 nan 0.0010 0.0002
## 480 1.0235 nan 0.0010 0.0002
## 500 1.0149 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0003
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1982 nan 0.0010 0.0003
## 180 1.1852 nan 0.0010 0.0003
## 200 1.1724 nan 0.0010 0.0003
## 220 1.1600 nan 0.0010 0.0003
## 240 1.1479 nan 0.0010 0.0003
## 260 1.1361 nan 0.0010 0.0003
## 280 1.1247 nan 0.0010 0.0002
## 300 1.1134 nan 0.0010 0.0003
## 320 1.1028 nan 0.0010 0.0003
## 340 1.0928 nan 0.0010 0.0002
## 360 1.0826 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0630 nan 0.0010 0.0002
## 420 1.0537 nan 0.0010 0.0002
## 440 1.0447 nan 0.0010 0.0002
## 460 1.0358 nan 0.0010 0.0002
## 480 1.0271 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0004
## 100 1.2365 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1770 nan 0.0010 0.0003
## 200 1.1635 nan 0.0010 0.0003
## 220 1.1503 nan 0.0010 0.0002
## 240 1.1372 nan 0.0010 0.0003
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## 280 1.1132 nan 0.0010 0.0003
## 300 1.1014 nan 0.0010 0.0003
## 320 1.0900 nan 0.0010 0.0003
## 340 1.0788 nan 0.0010 0.0002
## 360 1.0680 nan 0.0010 0.0003
## 380 1.0575 nan 0.0010 0.0002
## 400 1.0474 nan 0.0010 0.0002
## 420 1.0375 nan 0.0010 0.0002
## 440 1.0279 nan 0.0010 0.0002
## 460 1.0186 nan 0.0010 0.0002
## 480 1.0094 nan 0.0010 0.0002
## 500 1.0005 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0005
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2681 nan 0.0010 0.0004
## 80 1.2520 nan 0.0010 0.0004
## 100 1.2364 nan 0.0010 0.0003
## 120 1.2212 nan 0.0010 0.0003
## 140 1.2062 nan 0.0010 0.0003
## 160 1.1917 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1647 nan 0.0010 0.0003
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1390 nan 0.0010 0.0002
## 260 1.1266 nan 0.0010 0.0003
## 280 1.1147 nan 0.0010 0.0003
## 300 1.1030 nan 0.0010 0.0002
## 320 1.0915 nan 0.0010 0.0003
## 340 1.0804 nan 0.0010 0.0002
## 360 1.0699 nan 0.0010 0.0002
## 380 1.0593 nan 0.0010 0.0003
## 400 1.0491 nan 0.0010 0.0002
## 420 1.0395 nan 0.0010 0.0002
## 440 1.0299 nan 0.0010 0.0002
## 460 1.0207 nan 0.0010 0.0002
## 480 1.0115 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0003
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0003
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2367 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2070 nan 0.0010 0.0003
## 160 1.1929 nan 0.0010 0.0003
## 180 1.1793 nan 0.0010 0.0003
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## 220 1.1534 nan 0.0010 0.0002
## 240 1.1408 nan 0.0010 0.0003
## 260 1.1287 nan 0.0010 0.0003
## 280 1.1166 nan 0.0010 0.0003
## 300 1.1051 nan 0.0010 0.0002
## 320 1.0942 nan 0.0010 0.0002
## 340 1.0837 nan 0.0010 0.0002
## 360 1.0731 nan 0.0010 0.0002
## 380 1.0628 nan 0.0010 0.0002
## 400 1.0530 nan 0.0010 0.0002
## 420 1.0433 nan 0.0010 0.0002
## 440 1.0337 nan 0.0010 0.0002
## 460 1.0245 nan 0.0010 0.0002
## 480 1.0156 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3029 nan 0.0100 0.0041
## 3 1.2953 nan 0.0100 0.0036
## 4 1.2883 nan 0.0100 0.0033
## 5 1.2803 nan 0.0100 0.0036
## 6 1.2729 nan 0.0100 0.0029
## 7 1.2657 nan 0.0100 0.0033
## 8 1.2583 nan 0.0100 0.0033
## 9 1.2512 nan 0.0100 0.0029
## 10 1.2440 nan 0.0100 0.0029
## 20 1.1785 nan 0.0100 0.0028
## 40 1.0757 nan 0.0100 0.0020
## 60 0.9971 nan 0.0100 0.0015
## 80 0.9334 nan 0.0100 0.0010
## 100 0.8817 nan 0.0100 0.0007
## 120 0.8408 nan 0.0100 0.0008
## 140 0.8063 nan 0.0100 0.0006
## 160 0.7765 nan 0.0100 0.0004
## 180 0.7520 nan 0.0100 0.0004
## 200 0.7292 nan 0.0100 0.0002
## 220 0.7093 nan 0.0100 0.0000
## 240 0.6929 nan 0.0100 -0.0000
## 260 0.6775 nan 0.0100 0.0000
## 280 0.6639 nan 0.0100 0.0001
## 300 0.6509 nan 0.0100 0.0000
## 320 0.6379 nan 0.0100 -0.0000
## 340 0.6261 nan 0.0100 -0.0001
## 360 0.6149 nan 0.0100 0.0001
## 380 0.6038 nan 0.0100 -0.0001
## 400 0.5937 nan 0.0100 -0.0000
## 420 0.5840 nan 0.0100 -0.0000
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## 460 0.5654 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0042
## 2 1.3034 nan 0.0100 0.0036
## 3 1.2958 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0032
## 5 1.2804 nan 0.0100 0.0037
## 6 1.2735 nan 0.0100 0.0030
## 7 1.2665 nan 0.0100 0.0031
## 8 1.2592 nan 0.0100 0.0034
## 9 1.2522 nan 0.0100 0.0034
## 10 1.2449 nan 0.0100 0.0033
## 20 1.1796 nan 0.0100 0.0024
## 40 1.0769 nan 0.0100 0.0017
## 60 0.9979 nan 0.0100 0.0016
## 80 0.9347 nan 0.0100 0.0012
## 100 0.8849 nan 0.0100 0.0008
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## 140 0.8105 nan 0.0100 0.0005
## 160 0.7808 nan 0.0100 0.0001
## 180 0.7553 nan 0.0100 0.0003
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## 240 0.6972 nan 0.0100 0.0001
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## 280 0.6681 nan 0.0100 -0.0001
## 300 0.6545 nan 0.0100 0.0000
## 320 0.6425 nan 0.0100 0.0000
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## 360 0.6203 nan 0.0100 -0.0001
## 380 0.6090 nan 0.0100 -0.0000
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## 460 0.5713 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0041
## 2 1.3040 nan 0.0100 0.0039
## 3 1.2957 nan 0.0100 0.0039
## 4 1.2873 nan 0.0100 0.0032
## 5 1.2798 nan 0.0100 0.0035
## 6 1.2724 nan 0.0100 0.0032
## 7 1.2656 nan 0.0100 0.0034
## 8 1.2587 nan 0.0100 0.0034
## 9 1.2513 nan 0.0100 0.0031
## 10 1.2444 nan 0.0100 0.0030
## 20 1.1806 nan 0.0100 0.0027
## 40 1.0775 nan 0.0100 0.0020
## 60 0.9982 nan 0.0100 0.0013
## 80 0.9356 nan 0.0100 0.0012
## 100 0.8841 nan 0.0100 0.0010
## 120 0.8436 nan 0.0100 0.0007
## 140 0.8077 nan 0.0100 0.0004
## 160 0.7789 nan 0.0100 0.0003
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## 200 0.7333 nan 0.0100 0.0002
## 220 0.7156 nan 0.0100 0.0000
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## 340 0.6341 nan 0.0100 -0.0002
## 360 0.6224 nan 0.0100 -0.0000
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## 400 0.6020 nan 0.0100 0.0001
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## 460 0.5749 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3028 nan 0.0100 0.0036
## 3 1.2942 nan 0.0100 0.0038
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2776 nan 0.0100 0.0034
## 6 1.2698 nan 0.0100 0.0036
## 7 1.2629 nan 0.0100 0.0033
## 8 1.2551 nan 0.0100 0.0035
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## 20 1.1697 nan 0.0100 0.0030
## 40 1.0612 nan 0.0100 0.0018
## 60 0.9762 nan 0.0100 0.0014
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## 200 0.6895 nan 0.0100 0.0003
## 220 0.6680 nan 0.0100 0.0000
## 240 0.6484 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0039
## 2 1.3033 nan 0.0100 0.0038
## 3 1.2948 nan 0.0100 0.0038
## 4 1.2863 nan 0.0100 0.0037
## 5 1.2779 nan 0.0100 0.0038
## 6 1.2704 nan 0.0100 0.0034
## 7 1.2633 nan 0.0100 0.0032
## 8 1.2554 nan 0.0100 0.0035
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## 20 1.1724 nan 0.0100 0.0029
## 40 1.0628 nan 0.0100 0.0019
## 60 0.9781 nan 0.0100 0.0014
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## 340 0.5832 nan 0.0100 0.0002
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## 380 0.5599 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0042
## 2 1.3028 nan 0.0100 0.0040
## 3 1.2942 nan 0.0100 0.0037
## 4 1.2859 nan 0.0100 0.0034
## 5 1.2779 nan 0.0100 0.0035
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2630 nan 0.0100 0.0031
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## 9 1.2483 nan 0.0100 0.0029
## 10 1.2406 nan 0.0100 0.0033
## 20 1.1722 nan 0.0100 0.0027
## 40 1.0626 nan 0.0100 0.0020
## 60 0.9787 nan 0.0100 0.0017
## 80 0.9126 nan 0.0100 0.0013
## 100 0.8621 nan 0.0100 0.0007
## 120 0.8183 nan 0.0100 0.0006
## 140 0.7826 nan 0.0100 0.0007
## 160 0.7523 nan 0.0100 0.0003
## 180 0.7268 nan 0.0100 0.0003
## 200 0.7035 nan 0.0100 0.0002
## 220 0.6835 nan 0.0100 0.0000
## 240 0.6646 nan 0.0100 0.0002
## 260 0.6475 nan 0.0100 0.0000
## 280 0.6319 nan 0.0100 -0.0001
## 300 0.6174 nan 0.0100 -0.0001
## 320 0.6048 nan 0.0100 -0.0001
## 340 0.5929 nan 0.0100 -0.0001
## 360 0.5812 nan 0.0100 0.0003
## 380 0.5705 nan 0.0100 0.0000
## 400 0.5599 nan 0.0100 -0.0000
## 420 0.5492 nan 0.0100 0.0001
## 440 0.5383 nan 0.0100 0.0000
## 460 0.5295 nan 0.0100 0.0001
## 480 0.5202 nan 0.0100 -0.0001
## 500 0.5107 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2932 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0040
## 5 1.2759 nan 0.0100 0.0038
## 6 1.2673 nan 0.0100 0.0037
## 7 1.2586 nan 0.0100 0.0038
## 8 1.2501 nan 0.0100 0.0036
## 9 1.2418 nan 0.0100 0.0036
## 10 1.2341 nan 0.0100 0.0037
## 20 1.1612 nan 0.0100 0.0031
## 40 1.0443 nan 0.0100 0.0023
## 60 0.9556 nan 0.0100 0.0017
## 80 0.8873 nan 0.0100 0.0010
## 100 0.8324 nan 0.0100 0.0011
## 120 0.7860 nan 0.0100 0.0007
## 140 0.7480 nan 0.0100 0.0005
## 160 0.7145 nan 0.0100 0.0004
## 180 0.6846 nan 0.0100 0.0002
## 200 0.6589 nan 0.0100 0.0003
## 220 0.6351 nan 0.0100 0.0003
## 240 0.6151 nan 0.0100 0.0002
## 260 0.5969 nan 0.0100 0.0001
## 280 0.5795 nan 0.0100 0.0000
## 300 0.5631 nan 0.0100 0.0001
## 320 0.5477 nan 0.0100 -0.0000
## 340 0.5329 nan 0.0100 0.0000
## 360 0.5191 nan 0.0100 -0.0000
## 380 0.5071 nan 0.0100 0.0000
## 400 0.4956 nan 0.0100 0.0001
## 420 0.4847 nan 0.0100 -0.0001
## 440 0.4732 nan 0.0100 -0.0001
## 460 0.4626 nan 0.0100 0.0000
## 480 0.4518 nan 0.0100 -0.0002
## 500 0.4422 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2929 nan 0.0100 0.0043
## 4 1.2841 nan 0.0100 0.0034
## 5 1.2757 nan 0.0100 0.0038
## 6 1.2676 nan 0.0100 0.0038
## 7 1.2597 nan 0.0100 0.0033
## 8 1.2514 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0035
## 10 1.2356 nan 0.0100 0.0034
## 20 1.1646 nan 0.0100 0.0030
## 40 1.0480 nan 0.0100 0.0021
## 60 0.9604 nan 0.0100 0.0015
## 80 0.8912 nan 0.0100 0.0012
## 100 0.8362 nan 0.0100 0.0008
## 120 0.7907 nan 0.0100 0.0005
## 140 0.7519 nan 0.0100 0.0006
## 160 0.7191 nan 0.0100 0.0004
## 180 0.6922 nan 0.0100 0.0001
## 200 0.6664 nan 0.0100 -0.0001
## 220 0.6443 nan 0.0100 0.0001
## 240 0.6235 nan 0.0100 0.0001
## 260 0.6051 nan 0.0100 0.0001
## 280 0.5879 nan 0.0100 0.0000
## 300 0.5714 nan 0.0100 -0.0000
## 320 0.5562 nan 0.0100 -0.0001
## 340 0.5425 nan 0.0100 -0.0001
## 360 0.5298 nan 0.0100 0.0000
## 380 0.5166 nan 0.0100 -0.0000
## 400 0.5042 nan 0.0100 -0.0001
## 420 0.4932 nan 0.0100 0.0000
## 440 0.4828 nan 0.0100 0.0001
## 460 0.4731 nan 0.0100 -0.0000
## 480 0.4624 nan 0.0100 -0.0001
## 500 0.4519 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0039
## 4 1.2839 nan 0.0100 0.0039
## 5 1.2757 nan 0.0100 0.0040
## 6 1.2678 nan 0.0100 0.0034
## 7 1.2599 nan 0.0100 0.0039
## 8 1.2523 nan 0.0100 0.0032
## 9 1.2448 nan 0.0100 0.0034
## 10 1.2370 nan 0.0100 0.0040
## 20 1.1685 nan 0.0100 0.0027
## 40 1.0564 nan 0.0100 0.0023
## 60 0.9678 nan 0.0100 0.0015
## 80 0.8995 nan 0.0100 0.0012
## 100 0.8458 nan 0.0100 0.0012
## 120 0.8007 nan 0.0100 0.0008
## 140 0.7640 nan 0.0100 0.0005
## 160 0.7310 nan 0.0100 0.0004
## 180 0.7031 nan 0.0100 0.0003
## 200 0.6805 nan 0.0100 0.0002
## 220 0.6585 nan 0.0100 0.0001
## 240 0.6390 nan 0.0100 0.0001
## 260 0.6213 nan 0.0100 0.0001
## 280 0.6047 nan 0.0100 0.0001
## 300 0.5876 nan 0.0100 0.0001
## 320 0.5731 nan 0.0100 0.0000
## 340 0.5600 nan 0.0100 -0.0000
## 360 0.5466 nan 0.0100 -0.0001
## 380 0.5336 nan 0.0100 0.0002
## 400 0.5220 nan 0.0100 -0.0000
## 420 0.5110 nan 0.0100 -0.0001
## 440 0.4994 nan 0.0100 -0.0001
## 460 0.4880 nan 0.0100 0.0000
## 480 0.4783 nan 0.0100 -0.0001
## 500 0.4686 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2358 nan 0.1000 0.0361
## 2 1.1722 nan 0.1000 0.0271
## 3 1.1241 nan 0.1000 0.0208
## 4 1.0768 nan 0.1000 0.0178
## 5 1.0345 nan 0.1000 0.0170
## 6 0.9970 nan 0.1000 0.0151
## 7 0.9635 nan 0.1000 0.0134
## 8 0.9322 nan 0.1000 0.0127
## 9 0.9038 nan 0.1000 0.0103
## 10 0.8834 nan 0.1000 0.0058
## 20 0.7239 nan 0.1000 0.0003
## 40 0.5859 nan 0.1000 0.0006
## 60 0.5066 nan 0.1000 -0.0014
## 80 0.4385 nan 0.1000 -0.0016
## 100 0.3846 nan 0.1000 -0.0004
## 120 0.3490 nan 0.1000 -0.0008
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2814 nan 0.1000 -0.0001
## 180 0.2532 nan 0.1000 -0.0006
## 200 0.2285 nan 0.1000 0.0002
## 220 0.2057 nan 0.1000 -0.0007
## 240 0.1845 nan 0.1000 -0.0004
## 260 0.1663 nan 0.1000 -0.0005
## 280 0.1522 nan 0.1000 -0.0001
## 300 0.1384 nan 0.1000 -0.0004
## 320 0.1258 nan 0.1000 -0.0001
## 340 0.1145 nan 0.1000 0.0001
## 360 0.1043 nan 0.1000 0.0001
## 380 0.0962 nan 0.1000 -0.0001
## 400 0.0882 nan 0.1000 -0.0000
## 420 0.0813 nan 0.1000 -0.0003
## 440 0.0752 nan 0.1000 -0.0001
## 460 0.0695 nan 0.1000 -0.0002
## 480 0.0639 nan 0.1000 -0.0002
## 500 0.0592 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0368
## 2 1.1800 nan 0.1000 0.0257
## 3 1.1249 nan 0.1000 0.0253
## 4 1.0707 nan 0.1000 0.0227
## 5 1.0281 nan 0.1000 0.0169
## 6 0.9956 nan 0.1000 0.0148
## 7 0.9642 nan 0.1000 0.0109
## 8 0.9372 nan 0.1000 0.0108
## 9 0.9085 nan 0.1000 0.0116
## 10 0.8823 nan 0.1000 0.0095
## 20 0.7362 nan 0.1000 0.0015
## 40 0.6135 nan 0.1000 -0.0018
## 60 0.5279 nan 0.1000 -0.0005
## 80 0.4641 nan 0.1000 0.0003
## 100 0.4138 nan 0.1000 -0.0011
## 120 0.3682 nan 0.1000 -0.0022
## 140 0.3332 nan 0.1000 -0.0009
## 160 0.2992 nan 0.1000 -0.0010
## 180 0.2715 nan 0.1000 -0.0006
## 200 0.2462 nan 0.1000 -0.0003
## 220 0.2246 nan 0.1000 -0.0002
## 240 0.2051 nan 0.1000 -0.0006
## 260 0.1863 nan 0.1000 -0.0003
## 280 0.1709 nan 0.1000 -0.0005
## 300 0.1551 nan 0.1000 -0.0004
## 320 0.1436 nan 0.1000 -0.0006
## 340 0.1327 nan 0.1000 -0.0003
## 360 0.1214 nan 0.1000 -0.0006
## 380 0.1117 nan 0.1000 -0.0000
## 400 0.1024 nan 0.1000 -0.0003
## 420 0.0961 nan 0.1000 -0.0001
## 440 0.0881 nan 0.1000 -0.0001
## 460 0.0819 nan 0.1000 -0.0004
## 480 0.0760 nan 0.1000 -0.0001
## 500 0.0705 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2413 nan 0.1000 0.0360
## 2 1.1728 nan 0.1000 0.0297
## 3 1.1214 nan 0.1000 0.0216
## 4 1.0726 nan 0.1000 0.0212
## 5 1.0274 nan 0.1000 0.0192
## 6 0.9897 nan 0.1000 0.0161
## 7 0.9557 nan 0.1000 0.0147
## 8 0.9329 nan 0.1000 0.0087
## 9 0.9073 nan 0.1000 0.0091
## 10 0.8776 nan 0.1000 0.0128
## 20 0.7336 nan 0.1000 0.0023
## 40 0.6027 nan 0.1000 0.0001
## 60 0.5247 nan 0.1000 -0.0004
## 80 0.4603 nan 0.1000 0.0002
## 100 0.4095 nan 0.1000 0.0000
## 120 0.3634 nan 0.1000 0.0003
## 140 0.3272 nan 0.1000 -0.0009
## 160 0.2945 nan 0.1000 -0.0005
## 180 0.2709 nan 0.1000 -0.0009
## 200 0.2481 nan 0.1000 -0.0005
## 220 0.2256 nan 0.1000 -0.0006
## 240 0.2067 nan 0.1000 -0.0009
## 260 0.1874 nan 0.1000 -0.0001
## 280 0.1726 nan 0.1000 -0.0006
## 300 0.1604 nan 0.1000 -0.0007
## 320 0.1484 nan 0.1000 -0.0006
## 340 0.1383 nan 0.1000 -0.0002
## 360 0.1287 nan 0.1000 -0.0005
## 380 0.1189 nan 0.1000 -0.0003
## 400 0.1099 nan 0.1000 -0.0003
## 420 0.1003 nan 0.1000 -0.0003
## 440 0.0948 nan 0.1000 -0.0005
## 460 0.0884 nan 0.1000 -0.0003
## 480 0.0818 nan 0.1000 -0.0003
## 500 0.0767 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2407 nan 0.1000 0.0394
## 2 1.1664 nan 0.1000 0.0345
## 3 1.1055 nan 0.1000 0.0273
## 4 1.0577 nan 0.1000 0.0185
## 5 1.0167 nan 0.1000 0.0171
## 6 0.9801 nan 0.1000 0.0154
## 7 0.9461 nan 0.1000 0.0127
## 8 0.9154 nan 0.1000 0.0111
## 9 0.8882 nan 0.1000 0.0113
## 10 0.8582 nan 0.1000 0.0114
## 20 0.6922 nan 0.1000 0.0017
## 40 0.5419 nan 0.1000 0.0004
## 60 0.4596 nan 0.1000 -0.0013
## 80 0.3912 nan 0.1000 0.0003
## 100 0.3396 nan 0.1000 -0.0004
## 120 0.2887 nan 0.1000 -0.0007
## 140 0.2524 nan 0.1000 -0.0001
## 160 0.2200 nan 0.1000 -0.0008
## 180 0.1915 nan 0.1000 0.0000
## 200 0.1711 nan 0.1000 -0.0005
## 220 0.1515 nan 0.1000 0.0001
## 240 0.1326 nan 0.1000 -0.0001
## 260 0.1192 nan 0.1000 -0.0003
## 280 0.1069 nan 0.1000 -0.0003
## 300 0.0957 nan 0.1000 -0.0002
## 320 0.0838 nan 0.1000 0.0000
## 340 0.0760 nan 0.1000 -0.0000
## 360 0.0682 nan 0.1000 -0.0001
## 380 0.0622 nan 0.1000 -0.0001
## 400 0.0561 nan 0.1000 -0.0002
## 420 0.0504 nan 0.1000 -0.0001
## 440 0.0456 nan 0.1000 -0.0001
## 460 0.0413 nan 0.1000 -0.0001
## 480 0.0371 nan 0.1000 -0.0001
## 500 0.0338 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0357
## 2 1.1712 nan 0.1000 0.0289
## 3 1.1099 nan 0.1000 0.0272
## 4 1.0565 nan 0.1000 0.0207
## 5 1.0106 nan 0.1000 0.0189
## 6 0.9661 nan 0.1000 0.0191
## 7 0.9332 nan 0.1000 0.0117
## 8 0.9000 nan 0.1000 0.0116
## 9 0.8742 nan 0.1000 0.0095
## 10 0.8471 nan 0.1000 0.0103
## 20 0.6991 nan 0.1000 0.0026
## 40 0.5613 nan 0.1000 -0.0014
## 60 0.4725 nan 0.1000 -0.0013
## 80 0.4056 nan 0.1000 0.0002
## 100 0.3510 nan 0.1000 -0.0005
## 120 0.3024 nan 0.1000 -0.0010
## 140 0.2663 nan 0.1000 -0.0007
## 160 0.2359 nan 0.1000 -0.0007
## 180 0.2099 nan 0.1000 -0.0009
## 200 0.1861 nan 0.1000 -0.0005
## 220 0.1633 nan 0.1000 0.0001
## 240 0.1462 nan 0.1000 -0.0003
## 260 0.1301 nan 0.1000 -0.0004
## 280 0.1162 nan 0.1000 -0.0007
## 300 0.1056 nan 0.1000 -0.0004
## 320 0.0952 nan 0.1000 -0.0003
## 340 0.0859 nan 0.1000 -0.0005
## 360 0.0771 nan 0.1000 -0.0003
## 380 0.0694 nan 0.1000 -0.0002
## 400 0.0619 nan 0.1000 -0.0001
## 420 0.0557 nan 0.1000 -0.0002
## 440 0.0505 nan 0.1000 -0.0002
## 460 0.0457 nan 0.1000 -0.0002
## 480 0.0410 nan 0.1000 -0.0000
## 500 0.0370 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2387 nan 0.1000 0.0383
## 2 1.1700 nan 0.1000 0.0316
## 3 1.1112 nan 0.1000 0.0259
## 4 1.0615 nan 0.1000 0.0199
## 5 1.0132 nan 0.1000 0.0221
## 6 0.9741 nan 0.1000 0.0170
## 7 0.9389 nan 0.1000 0.0150
## 8 0.9125 nan 0.1000 0.0084
## 9 0.8851 nan 0.1000 0.0100
## 10 0.8644 nan 0.1000 0.0048
## 20 0.7091 nan 0.1000 0.0011
## 40 0.5621 nan 0.1000 -0.0020
## 60 0.4661 nan 0.1000 0.0008
## 80 0.3954 nan 0.1000 0.0000
## 100 0.3429 nan 0.1000 -0.0011
## 120 0.2972 nan 0.1000 -0.0009
## 140 0.2613 nan 0.1000 -0.0020
## 160 0.2318 nan 0.1000 -0.0005
## 180 0.2075 nan 0.1000 -0.0008
## 200 0.1850 nan 0.1000 -0.0008
## 220 0.1646 nan 0.1000 -0.0005
## 240 0.1495 nan 0.1000 -0.0010
## 260 0.1349 nan 0.1000 -0.0004
## 280 0.1215 nan 0.1000 -0.0004
## 300 0.1097 nan 0.1000 -0.0006
## 320 0.0981 nan 0.1000 -0.0003
## 340 0.0883 nan 0.1000 -0.0002
## 360 0.0795 nan 0.1000 -0.0002
## 380 0.0728 nan 0.1000 -0.0005
## 400 0.0657 nan 0.1000 -0.0001
## 420 0.0597 nan 0.1000 -0.0002
## 440 0.0540 nan 0.1000 -0.0001
## 460 0.0492 nan 0.1000 -0.0002
## 480 0.0444 nan 0.1000 -0.0001
## 500 0.0409 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2343 nan 0.1000 0.0362
## 2 1.1576 nan 0.1000 0.0301
## 3 1.0929 nan 0.1000 0.0277
## 4 1.0390 nan 0.1000 0.0227
## 5 0.9953 nan 0.1000 0.0196
## 6 0.9556 nan 0.1000 0.0172
## 7 0.9171 nan 0.1000 0.0139
## 8 0.8886 nan 0.1000 0.0111
## 9 0.8605 nan 0.1000 0.0094
## 10 0.8324 nan 0.1000 0.0106
## 20 0.6656 nan 0.1000 0.0025
## 40 0.5056 nan 0.1000 -0.0002
## 60 0.4084 nan 0.1000 -0.0001
## 80 0.3370 nan 0.1000 -0.0004
## 100 0.2806 nan 0.1000 -0.0001
## 120 0.2350 nan 0.1000 -0.0003
## 140 0.1994 nan 0.1000 -0.0005
## 160 0.1704 nan 0.1000 -0.0000
## 180 0.1460 nan 0.1000 0.0002
## 200 0.1282 nan 0.1000 -0.0004
## 220 0.1110 nan 0.1000 -0.0002
## 240 0.0973 nan 0.1000 -0.0003
## 260 0.0847 nan 0.1000 -0.0003
## 280 0.0741 nan 0.1000 -0.0001
## 300 0.0637 nan 0.1000 -0.0000
## 320 0.0559 nan 0.1000 -0.0001
## 340 0.0486 nan 0.1000 -0.0000
## 360 0.0433 nan 0.1000 -0.0002
## 380 0.0382 nan 0.1000 -0.0001
## 400 0.0339 nan 0.1000 -0.0001
## 420 0.0299 nan 0.1000 -0.0001
## 440 0.0269 nan 0.1000 -0.0000
## 460 0.0241 nan 0.1000 -0.0001
## 480 0.0215 nan 0.1000 -0.0000
## 500 0.0192 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2318 nan 0.1000 0.0412
## 2 1.1610 nan 0.1000 0.0307
## 3 1.0957 nan 0.1000 0.0271
## 4 1.0462 nan 0.1000 0.0202
## 5 0.9971 nan 0.1000 0.0196
## 6 0.9606 nan 0.1000 0.0137
## 7 0.9233 nan 0.1000 0.0135
## 8 0.8942 nan 0.1000 0.0111
## 9 0.8654 nan 0.1000 0.0119
## 10 0.8413 nan 0.1000 0.0083
## 20 0.6708 nan 0.1000 0.0020
## 40 0.5172 nan 0.1000 -0.0002
## 60 0.4258 nan 0.1000 -0.0000
## 80 0.3509 nan 0.1000 -0.0003
## 100 0.2907 nan 0.1000 -0.0002
## 120 0.2433 nan 0.1000 -0.0013
## 140 0.2096 nan 0.1000 -0.0006
## 160 0.1790 nan 0.1000 -0.0012
## 180 0.1568 nan 0.1000 -0.0005
## 200 0.1376 nan 0.1000 -0.0001
## 220 0.1209 nan 0.1000 -0.0004
## 240 0.1059 nan 0.1000 -0.0004
## 260 0.0917 nan 0.1000 -0.0002
## 280 0.0799 nan 0.1000 -0.0001
## 300 0.0705 nan 0.1000 -0.0003
## 320 0.0613 nan 0.1000 -0.0003
## 340 0.0538 nan 0.1000 -0.0001
## 360 0.0478 nan 0.1000 -0.0001
## 380 0.0425 nan 0.1000 -0.0002
## 400 0.0379 nan 0.1000 -0.0001
## 420 0.0331 nan 0.1000 -0.0001
## 440 0.0292 nan 0.1000 -0.0001
## 460 0.0266 nan 0.1000 -0.0001
## 480 0.0235 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2365 nan 0.1000 0.0381
## 2 1.1645 nan 0.1000 0.0325
## 3 1.1032 nan 0.1000 0.0220
## 4 1.0445 nan 0.1000 0.0248
## 5 1.0016 nan 0.1000 0.0186
## 6 0.9501 nan 0.1000 0.0209
## 7 0.9195 nan 0.1000 0.0121
## 8 0.8871 nan 0.1000 0.0109
## 9 0.8610 nan 0.1000 0.0103
## 10 0.8342 nan 0.1000 0.0102
## 20 0.6795 nan 0.1000 0.0027
## 40 0.5304 nan 0.1000 -0.0004
## 60 0.4416 nan 0.1000 -0.0012
## 80 0.3697 nan 0.1000 -0.0010
## 100 0.3057 nan 0.1000 -0.0005
## 120 0.2625 nan 0.1000 -0.0008
## 140 0.2282 nan 0.1000 -0.0007
## 160 0.1951 nan 0.1000 -0.0003
## 180 0.1697 nan 0.1000 -0.0004
## 200 0.1495 nan 0.1000 -0.0003
## 220 0.1312 nan 0.1000 -0.0007
## 240 0.1148 nan 0.1000 -0.0006
## 260 0.0999 nan 0.1000 -0.0006
## 280 0.0883 nan 0.1000 -0.0004
## 300 0.0789 nan 0.1000 -0.0003
## 320 0.0693 nan 0.1000 -0.0005
## 340 0.0612 nan 0.1000 -0.0002
## 360 0.0541 nan 0.1000 -0.0001
## 380 0.0484 nan 0.1000 -0.0003
## 400 0.0433 nan 0.1000 -0.0002
## 420 0.0382 nan 0.1000 -0.0001
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## 460 0.0304 nan 0.1000 -0.0001
## 480 0.0272 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0004
## 100 1.2396 nan 0.0010 0.0003
## 120 1.2251 nan 0.0010 0.0003
## 140 1.2113 nan 0.0010 0.0003
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1719 nan 0.0010 0.0003
## 220 1.1595 nan 0.0010 0.0002
## 240 1.1476 nan 0.0010 0.0003
## 260 1.1360 nan 0.0010 0.0003
## 280 1.1243 nan 0.0010 0.0003
## 300 1.1133 nan 0.0010 0.0002
## 320 1.1026 nan 0.0010 0.0002
## 340 1.0921 nan 0.0010 0.0002
## 360 1.0818 nan 0.0010 0.0002
## 380 1.0721 nan 0.0010 0.0002
## 400 1.0625 nan 0.0010 0.0002
## 420 1.0531 nan 0.0010 0.0002
## 440 1.0439 nan 0.0010 0.0002
## 460 1.0350 nan 0.0010 0.0002
## 480 1.0265 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0003
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0003
## 60 1.2712 nan 0.0010 0.0003
## 80 1.2554 nan 0.0010 0.0004
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2262 nan 0.0010 0.0003
## 140 1.2123 nan 0.0010 0.0003
## 160 1.1989 nan 0.0010 0.0003
## 180 1.1856 nan 0.0010 0.0003
## 200 1.1727 nan 0.0010 0.0003
## 220 1.1602 nan 0.0010 0.0003
## 240 1.1480 nan 0.0010 0.0003
## 260 1.1363 nan 0.0010 0.0003
## 280 1.1247 nan 0.0010 0.0003
## 300 1.1137 nan 0.0010 0.0002
## 320 1.1028 nan 0.0010 0.0003
## 340 1.0926 nan 0.0010 0.0002
## 360 1.0824 nan 0.0010 0.0002
## 380 1.0726 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0534 nan 0.0010 0.0002
## 440 1.0443 nan 0.0010 0.0002
## 460 1.0353 nan 0.0010 0.0002
## 480 1.0269 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0003
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2422 nan 0.0010 0.0003
## 120 1.2279 nan 0.0010 0.0003
## 140 1.2138 nan 0.0010 0.0003
## 160 1.2007 nan 0.0010 0.0003
## 180 1.1876 nan 0.0010 0.0003
## 200 1.1749 nan 0.0010 0.0002
## 220 1.1625 nan 0.0010 0.0002
## 240 1.1508 nan 0.0010 0.0003
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## 320 1.1067 nan 0.0010 0.0002
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## 380 1.0766 nan 0.0010 0.0002
## 400 1.0669 nan 0.0010 0.0002
## 420 1.0576 nan 0.0010 0.0002
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## 460 1.0398 nan 0.0010 0.0002
## 480 1.0311 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0005
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2348 nan 0.0010 0.0004
## 120 1.2191 nan 0.0010 0.0003
## 140 1.2041 nan 0.0010 0.0004
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1754 nan 0.0010 0.0003
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## 220 1.1492 nan 0.0010 0.0003
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## 280 1.1123 nan 0.0010 0.0003
## 300 1.1008 nan 0.0010 0.0002
## 320 1.0896 nan 0.0010 0.0002
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## 380 1.0576 nan 0.0010 0.0002
## 400 1.0476 nan 0.0010 0.0002
## 420 1.0376 nan 0.0010 0.0002
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## 460 1.0190 nan 0.0010 0.0002
## 480 1.0100 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0003
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2515 nan 0.0010 0.0004
## 100 1.2357 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2058 nan 0.0010 0.0003
## 160 1.1917 nan 0.0010 0.0003
## 180 1.1776 nan 0.0010 0.0003
## 200 1.1644 nan 0.0010 0.0003
## 220 1.1514 nan 0.0010 0.0003
## 240 1.1386 nan 0.0010 0.0003
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## 280 1.1146 nan 0.0010 0.0002
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## 320 1.0918 nan 0.0010 0.0003
## 340 1.0808 nan 0.0010 0.0002
## 360 1.0702 nan 0.0010 0.0002
## 380 1.0599 nan 0.0010 0.0002
## 400 1.0500 nan 0.0010 0.0002
## 420 1.0400 nan 0.0010 0.0002
## 440 1.0304 nan 0.0010 0.0002
## 460 1.0212 nan 0.0010 0.0002
## 480 1.0123 nan 0.0010 0.0002
## 500 1.0036 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2375 nan 0.0010 0.0003
## 120 1.2223 nan 0.0010 0.0003
## 140 1.2082 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1806 nan 0.0010 0.0003
## 200 1.1675 nan 0.0010 0.0003
## 220 1.1543 nan 0.0010 0.0003
## 240 1.1417 nan 0.0010 0.0002
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## 320 1.0951 nan 0.0010 0.0003
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## 360 1.0735 nan 0.0010 0.0002
## 380 1.0633 nan 0.0010 0.0002
## 400 1.0533 nan 0.0010 0.0002
## 420 1.0436 nan 0.0010 0.0002
## 440 1.0343 nan 0.0010 0.0002
## 460 1.0251 nan 0.0010 0.0002
## 480 1.0161 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3156 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3127 nan 0.0010 0.0004
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2489 nan 0.0010 0.0004
## 100 1.2325 nan 0.0010 0.0004
## 120 1.2164 nan 0.0010 0.0004
## 140 1.2009 nan 0.0010 0.0003
## 160 1.1856 nan 0.0010 0.0003
## 180 1.1707 nan 0.0010 0.0003
## 200 1.1566 nan 0.0010 0.0003
## 220 1.1428 nan 0.0010 0.0003
## 240 1.1296 nan 0.0010 0.0003
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## 280 1.1040 nan 0.0010 0.0003
## 300 1.0919 nan 0.0010 0.0002
## 320 1.0801 nan 0.0010 0.0003
## 340 1.0687 nan 0.0010 0.0002
## 360 1.0576 nan 0.0010 0.0002
## 380 1.0465 nan 0.0010 0.0002
## 400 1.0360 nan 0.0010 0.0002
## 420 1.0259 nan 0.0010 0.0002
## 440 1.0160 nan 0.0010 0.0002
## 460 1.0066 nan 0.0010 0.0002
## 480 0.9971 nan 0.0010 0.0002
## 500 0.9881 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3195 nan 0.0010 0.0005
## 2 1.3185 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0004
## 4 1.3166 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0005
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2487 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2163 nan 0.0010 0.0003
## 140 1.2013 nan 0.0010 0.0003
## 160 1.1870 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1587 nan 0.0010 0.0003
## 220 1.1453 nan 0.0010 0.0003
## 240 1.1322 nan 0.0010 0.0003
## 260 1.1194 nan 0.0010 0.0003
## 280 1.1071 nan 0.0010 0.0003
## 300 1.0951 nan 0.0010 0.0002
## 320 1.0834 nan 0.0010 0.0003
## 340 1.0722 nan 0.0010 0.0002
## 360 1.0610 nan 0.0010 0.0002
## 380 1.0502 nan 0.0010 0.0002
## 400 1.0399 nan 0.0010 0.0002
## 420 1.0295 nan 0.0010 0.0003
## 440 1.0196 nan 0.0010 0.0002
## 460 1.0100 nan 0.0010 0.0002
## 480 1.0006 nan 0.0010 0.0002
## 500 0.9917 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0004
## 80 1.2504 nan 0.0010 0.0004
## 100 1.2341 nan 0.0010 0.0004
## 120 1.2187 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1886 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1608 nan 0.0010 0.0003
## 220 1.1476 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0003
## 280 1.1101 nan 0.0010 0.0003
## 300 1.0984 nan 0.0010 0.0003
## 320 1.0867 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0003
## 360 1.0647 nan 0.0010 0.0003
## 380 1.0542 nan 0.0010 0.0002
## 400 1.0439 nan 0.0010 0.0002
## 420 1.0338 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0145 nan 0.0010 0.0002
## 480 1.0052 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0040
## 4 1.2845 nan 0.0100 0.0039
## 5 1.2767 nan 0.0100 0.0038
## 6 1.2686 nan 0.0100 0.0041
## 7 1.2606 nan 0.0100 0.0036
## 8 1.2530 nan 0.0100 0.0033
## 9 1.2450 nan 0.0100 0.0037
## 10 1.2376 nan 0.0100 0.0034
## 20 1.1697 nan 0.0100 0.0032
## 40 1.0623 nan 0.0100 0.0018
## 60 0.9793 nan 0.0100 0.0017
## 80 0.9151 nan 0.0100 0.0012
## 100 0.8631 nan 0.0100 0.0008
## 120 0.8212 nan 0.0100 0.0007
## 140 0.7862 nan 0.0100 0.0008
## 160 0.7559 nan 0.0100 0.0005
## 180 0.7292 nan 0.0100 0.0004
## 200 0.7067 nan 0.0100 0.0002
## 220 0.6865 nan 0.0100 0.0002
## 240 0.6690 nan 0.0100 0.0002
## 260 0.6523 nan 0.0100 0.0001
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## 300 0.6249 nan 0.0100 0.0002
## 320 0.6120 nan 0.0100 0.0000
## 340 0.5996 nan 0.0100 0.0001
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## 380 0.5765 nan 0.0100 0.0001
## 400 0.5665 nan 0.0100 -0.0001
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## 440 0.5484 nan 0.0100 0.0001
## 460 0.5386 nan 0.0100 0.0000
## 480 0.5296 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0037
## 2 1.3037 nan 0.0100 0.0043
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2872 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0032
## 6 1.2709 nan 0.0100 0.0039
## 7 1.2627 nan 0.0100 0.0039
## 8 1.2547 nan 0.0100 0.0035
## 9 1.2470 nan 0.0100 0.0038
## 10 1.2390 nan 0.0100 0.0036
## 20 1.1732 nan 0.0100 0.0029
## 40 1.0646 nan 0.0100 0.0020
## 60 0.9808 nan 0.0100 0.0014
## 80 0.9160 nan 0.0100 0.0013
## 100 0.8636 nan 0.0100 0.0010
## 120 0.8213 nan 0.0100 0.0006
## 140 0.7858 nan 0.0100 0.0006
## 160 0.7551 nan 0.0100 0.0003
## 180 0.7302 nan 0.0100 0.0003
## 200 0.7077 nan 0.0100 0.0002
## 220 0.6870 nan 0.0100 0.0002
## 240 0.6699 nan 0.0100 0.0000
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## 280 0.6402 nan 0.0100 0.0001
## 300 0.6277 nan 0.0100 0.0000
## 320 0.6156 nan 0.0100 -0.0000
## 340 0.6039 nan 0.0100 -0.0001
## 360 0.5921 nan 0.0100 0.0000
## 380 0.5819 nan 0.0100 -0.0000
## 400 0.5713 nan 0.0100 0.0001
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## 460 0.5430 nan 0.0100 -0.0000
## 480 0.5345 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0038
## 2 1.3045 nan 0.0100 0.0037
## 3 1.2967 nan 0.0100 0.0036
## 4 1.2886 nan 0.0100 0.0033
## 5 1.2801 nan 0.0100 0.0041
## 6 1.2729 nan 0.0100 0.0034
## 7 1.2652 nan 0.0100 0.0038
## 8 1.2581 nan 0.0100 0.0031
## 9 1.2502 nan 0.0100 0.0036
## 10 1.2435 nan 0.0100 0.0032
## 20 1.1751 nan 0.0100 0.0029
## 40 1.0669 nan 0.0100 0.0022
## 60 0.9828 nan 0.0100 0.0014
## 80 0.9167 nan 0.0100 0.0010
## 100 0.8652 nan 0.0100 0.0008
## 120 0.8216 nan 0.0100 0.0007
## 140 0.7876 nan 0.0100 0.0005
## 160 0.7590 nan 0.0100 0.0003
## 180 0.7342 nan 0.0100 0.0000
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## 220 0.6945 nan 0.0100 0.0000
## 240 0.6775 nan 0.0100 0.0001
## 260 0.6623 nan 0.0100 -0.0001
## 280 0.6478 nan 0.0100 0.0001
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## 320 0.6223 nan 0.0100 -0.0001
## 340 0.6105 nan 0.0100 0.0001
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## 380 0.5901 nan 0.0100 -0.0000
## 400 0.5810 nan 0.0100 -0.0000
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## 460 0.5555 nan 0.0100 0.0000
## 480 0.5476 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3017 nan 0.0100 0.0046
## 3 1.2927 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0033
## 5 1.2755 nan 0.0100 0.0041
## 6 1.2670 nan 0.0100 0.0039
## 7 1.2587 nan 0.0100 0.0039
## 8 1.2502 nan 0.0100 0.0039
## 9 1.2419 nan 0.0100 0.0036
## 10 1.2350 nan 0.0100 0.0031
## 20 1.1629 nan 0.0100 0.0031
## 40 1.0474 nan 0.0100 0.0020
## 60 0.9599 nan 0.0100 0.0017
## 80 0.8905 nan 0.0100 0.0009
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## 120 0.7937 nan 0.0100 0.0006
## 140 0.7572 nan 0.0100 0.0006
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## 180 0.6979 nan 0.0100 0.0001
## 200 0.6743 nan 0.0100 0.0002
## 220 0.6530 nan 0.0100 0.0001
## 240 0.6337 nan 0.0100 0.0002
## 260 0.6164 nan 0.0100 -0.0001
## 280 0.6000 nan 0.0100 0.0001
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## 320 0.5706 nan 0.0100 0.0002
## 340 0.5580 nan 0.0100 0.0001
## 360 0.5453 nan 0.0100 -0.0000
## 380 0.5320 nan 0.0100 0.0001
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## 440 0.4999 nan 0.0100 0.0000
## 460 0.4903 nan 0.0100 -0.0001
## 480 0.4808 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0046
## 2 1.3016 nan 0.0100 0.0043
## 3 1.2925 nan 0.0100 0.0034
## 4 1.2841 nan 0.0100 0.0038
## 5 1.2753 nan 0.0100 0.0039
## 6 1.2670 nan 0.0100 0.0033
## 7 1.2588 nan 0.0100 0.0036
## 8 1.2514 nan 0.0100 0.0030
## 9 1.2429 nan 0.0100 0.0038
## 10 1.2338 nan 0.0100 0.0037
## 20 1.1625 nan 0.0100 0.0026
## 40 1.0489 nan 0.0100 0.0022
## 60 0.9607 nan 0.0100 0.0017
## 80 0.8948 nan 0.0100 0.0010
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## 120 0.7963 nan 0.0100 0.0007
## 140 0.7606 nan 0.0100 0.0001
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## 180 0.7029 nan 0.0100 0.0002
## 200 0.6790 nan 0.0100 0.0003
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## 280 0.6074 nan 0.0100 0.0000
## 300 0.5927 nan 0.0100 -0.0001
## 320 0.5777 nan 0.0100 0.0000
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## 460 0.4997 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3023 nan 0.0100 0.0039
## 3 1.2941 nan 0.0100 0.0037
## 4 1.2851 nan 0.0100 0.0041
## 5 1.2769 nan 0.0100 0.0037
## 6 1.2682 nan 0.0100 0.0039
## 7 1.2600 nan 0.0100 0.0034
## 8 1.2521 nan 0.0100 0.0039
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## 20 1.1671 nan 0.0100 0.0027
## 40 1.0524 nan 0.0100 0.0021
## 60 0.9658 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3013 nan 0.0100 0.0042
## 3 1.2915 nan 0.0100 0.0044
## 4 1.2825 nan 0.0100 0.0041
## 5 1.2745 nan 0.0100 0.0034
## 6 1.2649 nan 0.0100 0.0043
## 7 1.2563 nan 0.0100 0.0037
## 8 1.2475 nan 0.0100 0.0039
## 9 1.2384 nan 0.0100 0.0042
## 10 1.2300 nan 0.0100 0.0032
## 20 1.1562 nan 0.0100 0.0029
## 40 1.0377 nan 0.0100 0.0022
## 60 0.9459 nan 0.0100 0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3006 nan 0.0100 0.0045
## 3 1.2910 nan 0.0100 0.0044
## 4 1.2825 nan 0.0100 0.0039
## 5 1.2739 nan 0.0100 0.0039
## 6 1.2649 nan 0.0100 0.0040
## 7 1.2559 nan 0.0100 0.0043
## 8 1.2478 nan 0.0100 0.0039
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## 10 1.2315 nan 0.0100 0.0032
## 20 1.1578 nan 0.0100 0.0029
## 40 1.0380 nan 0.0100 0.0023
## 60 0.9469 nan 0.0100 0.0013
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## 320 0.5397 nan 0.0100 -0.0001
## 340 0.5253 nan 0.0100 0.0000
## 360 0.5127 nan 0.0100 0.0000
## 380 0.5000 nan 0.0100 0.0000
## 400 0.4884 nan 0.0100 0.0002
## 420 0.4760 nan 0.0100 0.0001
## 440 0.4651 nan 0.0100 0.0000
## 460 0.4541 nan 0.0100 -0.0000
## 480 0.4436 nan 0.0100 -0.0000
## 500 0.4345 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2925 nan 0.0100 0.0039
## 4 1.2837 nan 0.0100 0.0038
## 5 1.2748 nan 0.0100 0.0041
## 6 1.2662 nan 0.0100 0.0037
## 7 1.2577 nan 0.0100 0.0037
## 8 1.2495 nan 0.0100 0.0034
## 9 1.2406 nan 0.0100 0.0041
## 10 1.2322 nan 0.0100 0.0040
## 20 1.1593 nan 0.0100 0.0029
## 40 1.0397 nan 0.0100 0.0023
## 60 0.9512 nan 0.0100 0.0016
## 80 0.8820 nan 0.0100 0.0011
## 100 0.8264 nan 0.0100 0.0009
## 120 0.7809 nan 0.0100 0.0003
## 140 0.7425 nan 0.0100 0.0004
## 160 0.7099 nan 0.0100 0.0006
## 180 0.6828 nan 0.0100 0.0004
## 200 0.6597 nan 0.0100 0.0003
## 220 0.6382 nan 0.0100 0.0003
## 240 0.6182 nan 0.0100 -0.0000
## 260 0.5991 nan 0.0100 0.0003
## 280 0.5824 nan 0.0100 -0.0000
## 300 0.5667 nan 0.0100 0.0001
## 320 0.5520 nan 0.0100 -0.0000
## 340 0.5389 nan 0.0100 0.0001
## 360 0.5258 nan 0.0100 -0.0000
## 380 0.5128 nan 0.0100 0.0001
## 400 0.5015 nan 0.0100 0.0002
## 420 0.4906 nan 0.0100 -0.0001
## 440 0.4803 nan 0.0100 0.0001
## 460 0.4696 nan 0.0100 -0.0000
## 480 0.4597 nan 0.0100 -0.0000
## 500 0.4494 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2384 nan 0.1000 0.0431
## 2 1.1693 nan 0.1000 0.0291
## 3 1.1079 nan 0.1000 0.0285
## 4 1.0560 nan 0.1000 0.0217
## 5 1.0152 nan 0.1000 0.0170
## 6 0.9729 nan 0.1000 0.0166
## 7 0.9383 nan 0.1000 0.0139
## 8 0.9130 nan 0.1000 0.0087
## 9 0.8868 nan 0.1000 0.0110
## 10 0.8621 nan 0.1000 0.0093
## 20 0.7071 nan 0.1000 0.0022
## 40 0.5644 nan 0.1000 -0.0002
## 60 0.4820 nan 0.1000 -0.0009
## 80 0.4195 nan 0.1000 -0.0007
## 100 0.3751 nan 0.1000 -0.0011
## 120 0.3285 nan 0.1000 -0.0007
## 140 0.2930 nan 0.1000 -0.0002
## 160 0.2609 nan 0.1000 -0.0003
## 180 0.2329 nan 0.1000 0.0002
## 200 0.2109 nan 0.1000 -0.0008
## 220 0.1884 nan 0.1000 -0.0002
## 240 0.1725 nan 0.1000 -0.0001
## 260 0.1547 nan 0.1000 -0.0004
## 280 0.1406 nan 0.1000 -0.0002
## 300 0.1294 nan 0.1000 -0.0003
## 320 0.1181 nan 0.1000 -0.0004
## 340 0.1086 nan 0.1000 -0.0002
## 360 0.0996 nan 0.1000 -0.0005
## 380 0.0909 nan 0.1000 -0.0001
## 400 0.0833 nan 0.1000 -0.0005
## 420 0.0764 nan 0.1000 -0.0001
## 440 0.0696 nan 0.1000 -0.0001
## 460 0.0639 nan 0.1000 -0.0002
## 480 0.0587 nan 0.1000 -0.0002
## 500 0.0544 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0358
## 2 1.1624 nan 0.1000 0.0333
## 3 1.1126 nan 0.1000 0.0234
## 4 1.0607 nan 0.1000 0.0211
## 5 1.0143 nan 0.1000 0.0187
## 6 0.9817 nan 0.1000 0.0141
## 7 0.9481 nan 0.1000 0.0151
## 8 0.9182 nan 0.1000 0.0105
## 9 0.8900 nan 0.1000 0.0109
## 10 0.8650 nan 0.1000 0.0093
## 20 0.7143 nan 0.1000 0.0023
## 40 0.5757 nan 0.1000 0.0006
## 60 0.4900 nan 0.1000 -0.0007
## 80 0.4296 nan 0.1000 -0.0011
## 100 0.3731 nan 0.1000 -0.0003
## 120 0.3336 nan 0.1000 -0.0012
## 140 0.2985 nan 0.1000 -0.0007
## 160 0.2714 nan 0.1000 -0.0006
## 180 0.2404 nan 0.1000 -0.0007
## 200 0.2179 nan 0.1000 -0.0009
## 220 0.1975 nan 0.1000 -0.0004
## 240 0.1801 nan 0.1000 -0.0003
## 260 0.1646 nan 0.1000 -0.0008
## 280 0.1505 nan 0.1000 -0.0004
## 300 0.1388 nan 0.1000 -0.0003
## 320 0.1265 nan 0.1000 -0.0005
## 340 0.1155 nan 0.1000 -0.0003
## 360 0.1065 nan 0.1000 -0.0001
## 380 0.0977 nan 0.1000 0.0001
## 400 0.0890 nan 0.1000 -0.0003
## 420 0.0812 nan 0.1000 0.0000
## 440 0.0745 nan 0.1000 -0.0002
## 460 0.0690 nan 0.1000 -0.0000
## 480 0.0633 nan 0.1000 -0.0002
## 500 0.0589 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0364
## 2 1.1662 nan 0.1000 0.0274
## 3 1.1088 nan 0.1000 0.0243
## 4 1.0578 nan 0.1000 0.0252
## 5 1.0166 nan 0.1000 0.0174
## 6 0.9795 nan 0.1000 0.0148
## 7 0.9466 nan 0.1000 0.0149
## 8 0.9170 nan 0.1000 0.0116
## 9 0.8878 nan 0.1000 0.0113
## 10 0.8660 nan 0.1000 0.0071
## 20 0.7104 nan 0.1000 0.0038
## 40 0.5861 nan 0.1000 -0.0001
## 60 0.5076 nan 0.1000 -0.0020
## 80 0.4489 nan 0.1000 -0.0010
## 100 0.3963 nan 0.1000 -0.0013
## 120 0.3562 nan 0.1000 -0.0005
## 140 0.3132 nan 0.1000 -0.0007
## 160 0.2816 nan 0.1000 -0.0001
## 180 0.2546 nan 0.1000 -0.0008
## 200 0.2304 nan 0.1000 -0.0011
## 220 0.2077 nan 0.1000 -0.0005
## 240 0.1923 nan 0.1000 -0.0008
## 260 0.1766 nan 0.1000 -0.0005
## 280 0.1644 nan 0.1000 -0.0009
## 300 0.1503 nan 0.1000 -0.0007
## 320 0.1386 nan 0.1000 -0.0005
## 340 0.1275 nan 0.1000 -0.0004
## 360 0.1160 nan 0.1000 -0.0003
## 380 0.1081 nan 0.1000 -0.0003
## 400 0.0998 nan 0.1000 -0.0003
## 420 0.0924 nan 0.1000 -0.0003
## 440 0.0857 nan 0.1000 -0.0002
## 460 0.0789 nan 0.1000 -0.0001
## 480 0.0732 nan 0.1000 -0.0002
## 500 0.0676 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2305 nan 0.1000 0.0379
## 2 1.1606 nan 0.1000 0.0328
## 3 1.1010 nan 0.1000 0.0268
## 4 1.0459 nan 0.1000 0.0245
## 5 0.9947 nan 0.1000 0.0220
## 6 0.9569 nan 0.1000 0.0120
## 7 0.9196 nan 0.1000 0.0140
## 8 0.8895 nan 0.1000 0.0131
## 9 0.8597 nan 0.1000 0.0118
## 10 0.8343 nan 0.1000 0.0096
## 20 0.6732 nan 0.1000 0.0057
## 40 0.5263 nan 0.1000 -0.0015
## 60 0.4333 nan 0.1000 0.0011
## 80 0.3679 nan 0.1000 0.0002
## 100 0.3060 nan 0.1000 -0.0013
## 120 0.2614 nan 0.1000 -0.0001
## 140 0.2277 nan 0.1000 -0.0009
## 160 0.1969 nan 0.1000 -0.0007
## 180 0.1717 nan 0.1000 -0.0012
## 200 0.1511 nan 0.1000 -0.0003
## 220 0.1350 nan 0.1000 -0.0001
## 240 0.1209 nan 0.1000 -0.0003
## 260 0.1087 nan 0.1000 -0.0003
## 280 0.0968 nan 0.1000 -0.0004
## 300 0.0863 nan 0.1000 0.0001
## 320 0.0765 nan 0.1000 -0.0000
## 340 0.0694 nan 0.1000 -0.0003
## 360 0.0611 nan 0.1000 0.0000
## 380 0.0549 nan 0.1000 -0.0002
## 400 0.0492 nan 0.1000 -0.0002
## 420 0.0444 nan 0.1000 -0.0001
## 440 0.0402 nan 0.1000 -0.0002
## 460 0.0362 nan 0.1000 -0.0000
## 480 0.0326 nan 0.1000 -0.0000
## 500 0.0295 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2335 nan 0.1000 0.0402
## 2 1.1649 nan 0.1000 0.0314
## 3 1.0973 nan 0.1000 0.0310
## 4 1.0445 nan 0.1000 0.0232
## 5 0.9977 nan 0.1000 0.0190
## 6 0.9551 nan 0.1000 0.0182
## 7 0.9189 nan 0.1000 0.0118
## 8 0.8910 nan 0.1000 0.0090
## 9 0.8642 nan 0.1000 0.0111
## 10 0.8401 nan 0.1000 0.0089
## 20 0.6810 nan 0.1000 0.0015
## 40 0.5421 nan 0.1000 -0.0001
## 60 0.4520 nan 0.1000 -0.0003
## 80 0.3825 nan 0.1000 -0.0018
## 100 0.3270 nan 0.1000 -0.0006
## 120 0.2832 nan 0.1000 -0.0004
## 140 0.2428 nan 0.1000 -0.0007
## 160 0.2131 nan 0.1000 -0.0003
## 180 0.1859 nan 0.1000 -0.0007
## 200 0.1638 nan 0.1000 -0.0006
## 220 0.1429 nan 0.1000 -0.0007
## 240 0.1286 nan 0.1000 -0.0006
## 260 0.1132 nan 0.1000 -0.0000
## 280 0.0986 nan 0.1000 -0.0003
## 300 0.0888 nan 0.1000 -0.0002
## 320 0.0798 nan 0.1000 -0.0001
## 340 0.0712 nan 0.1000 -0.0003
## 360 0.0637 nan 0.1000 -0.0000
## 380 0.0579 nan 0.1000 -0.0002
## 400 0.0520 nan 0.1000 -0.0002
## 420 0.0470 nan 0.1000 -0.0002
## 440 0.0421 nan 0.1000 -0.0000
## 460 0.0377 nan 0.1000 -0.0001
## 480 0.0341 nan 0.1000 -0.0001
## 500 0.0307 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0396
## 2 1.1614 nan 0.1000 0.0327
## 3 1.0967 nan 0.1000 0.0287
## 4 1.0451 nan 0.1000 0.0228
## 5 1.0012 nan 0.1000 0.0171
## 6 0.9591 nan 0.1000 0.0170
## 7 0.9218 nan 0.1000 0.0145
## 8 0.8938 nan 0.1000 0.0117
## 9 0.8646 nan 0.1000 0.0106
## 10 0.8433 nan 0.1000 0.0080
## 20 0.6923 nan 0.1000 0.0019
## 40 0.5484 nan 0.1000 0.0001
## 60 0.4579 nan 0.1000 -0.0005
## 80 0.3872 nan 0.1000 -0.0007
## 100 0.3368 nan 0.1000 -0.0004
## 120 0.2916 nan 0.1000 0.0001
## 140 0.2577 nan 0.1000 -0.0004
## 160 0.2281 nan 0.1000 -0.0006
## 180 0.1987 nan 0.1000 -0.0009
## 200 0.1746 nan 0.1000 -0.0006
## 220 0.1562 nan 0.1000 -0.0003
## 240 0.1404 nan 0.1000 -0.0006
## 260 0.1261 nan 0.1000 -0.0002
## 280 0.1114 nan 0.1000 -0.0004
## 300 0.1003 nan 0.1000 -0.0005
## 320 0.0907 nan 0.1000 -0.0005
## 340 0.0813 nan 0.1000 -0.0003
## 360 0.0726 nan 0.1000 -0.0000
## 380 0.0651 nan 0.1000 -0.0000
## 400 0.0587 nan 0.1000 -0.0004
## 420 0.0531 nan 0.1000 -0.0002
## 440 0.0478 nan 0.1000 -0.0001
## 460 0.0431 nan 0.1000 -0.0002
## 480 0.0395 nan 0.1000 -0.0003
## 500 0.0359 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.1000 0.0448
## 2 1.1526 nan 0.1000 0.0362
## 3 1.0851 nan 0.1000 0.0291
## 4 1.0251 nan 0.1000 0.0263
## 5 0.9742 nan 0.1000 0.0198
## 6 0.9347 nan 0.1000 0.0174
## 7 0.8966 nan 0.1000 0.0163
## 8 0.8654 nan 0.1000 0.0133
## 9 0.8368 nan 0.1000 0.0103
## 10 0.8085 nan 0.1000 0.0093
## 20 0.6385 nan 0.1000 0.0035
## 40 0.4912 nan 0.1000 0.0003
## 60 0.3941 nan 0.1000 -0.0011
## 80 0.3160 nan 0.1000 0.0004
## 100 0.2621 nan 0.1000 0.0001
## 120 0.2186 nan 0.1000 -0.0005
## 140 0.1877 nan 0.1000 -0.0004
## 160 0.1568 nan 0.1000 -0.0004
## 180 0.1366 nan 0.1000 -0.0004
## 200 0.1178 nan 0.1000 -0.0004
## 220 0.1000 nan 0.1000 -0.0001
## 240 0.0871 nan 0.1000 -0.0002
## 260 0.0771 nan 0.1000 -0.0004
## 280 0.0682 nan 0.1000 -0.0003
## 300 0.0597 nan 0.1000 0.0000
## 320 0.0518 nan 0.1000 -0.0000
## 340 0.0453 nan 0.1000 -0.0001
## 360 0.0392 nan 0.1000 -0.0001
## 380 0.0345 nan 0.1000 0.0001
## 400 0.0302 nan 0.1000 -0.0000
## 420 0.0266 nan 0.1000 -0.0000
## 440 0.0233 nan 0.1000 -0.0001
## 460 0.0205 nan 0.1000 -0.0000
## 480 0.0179 nan 0.1000 -0.0001
## 500 0.0159 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2242 nan 0.1000 0.0420
## 2 1.1512 nan 0.1000 0.0342
## 3 1.0894 nan 0.1000 0.0290
## 4 1.0329 nan 0.1000 0.0238
## 5 0.9820 nan 0.1000 0.0205
## 6 0.9435 nan 0.1000 0.0146
## 7 0.9106 nan 0.1000 0.0125
## 8 0.8719 nan 0.1000 0.0148
## 9 0.8426 nan 0.1000 0.0131
## 10 0.8164 nan 0.1000 0.0092
## 20 0.6527 nan 0.1000 0.0023
## 40 0.4884 nan 0.1000 -0.0002
## 60 0.4010 nan 0.1000 0.0013
## 80 0.3218 nan 0.1000 -0.0002
## 100 0.2652 nan 0.1000 -0.0010
## 120 0.2301 nan 0.1000 -0.0010
## 140 0.1940 nan 0.1000 -0.0005
## 160 0.1695 nan 0.1000 -0.0006
## 180 0.1452 nan 0.1000 -0.0006
## 200 0.1241 nan 0.1000 -0.0002
## 220 0.1071 nan 0.1000 -0.0006
## 240 0.0943 nan 0.1000 0.0001
## 260 0.0827 nan 0.1000 -0.0005
## 280 0.0729 nan 0.1000 -0.0006
## 300 0.0635 nan 0.1000 -0.0003
## 320 0.0553 nan 0.1000 -0.0002
## 340 0.0489 nan 0.1000 -0.0001
## 360 0.0429 nan 0.1000 0.0000
## 380 0.0374 nan 0.1000 -0.0001
## 400 0.0325 nan 0.1000 -0.0001
## 420 0.0288 nan 0.1000 -0.0001
## 440 0.0254 nan 0.1000 -0.0000
## 460 0.0225 nan 0.1000 -0.0001
## 480 0.0199 nan 0.1000 -0.0001
## 500 0.0178 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0423
## 2 1.1583 nan 0.1000 0.0353
## 3 1.0942 nan 0.1000 0.0261
## 4 1.0363 nan 0.1000 0.0253
## 5 0.9896 nan 0.1000 0.0192
## 6 0.9486 nan 0.1000 0.0170
## 7 0.9115 nan 0.1000 0.0150
## 8 0.8810 nan 0.1000 0.0126
## 9 0.8504 nan 0.1000 0.0116
## 10 0.8206 nan 0.1000 0.0121
## 20 0.6615 nan 0.1000 0.0024
## 40 0.5043 nan 0.1000 -0.0009
## 60 0.4036 nan 0.1000 -0.0005
## 80 0.3387 nan 0.1000 -0.0008
## 100 0.2872 nan 0.1000 -0.0013
## 120 0.2439 nan 0.1000 0.0003
## 140 0.2083 nan 0.1000 -0.0005
## 160 0.1770 nan 0.1000 -0.0005
## 180 0.1524 nan 0.1000 -0.0005
## 200 0.1315 nan 0.1000 -0.0004
## 220 0.1153 nan 0.1000 -0.0004
## 240 0.1014 nan 0.1000 -0.0004
## 260 0.0891 nan 0.1000 -0.0004
## 280 0.0792 nan 0.1000 -0.0001
## 300 0.0693 nan 0.1000 -0.0005
## 320 0.0602 nan 0.1000 -0.0001
## 340 0.0534 nan 0.1000 -0.0003
## 360 0.0472 nan 0.1000 -0.0002
## 380 0.0418 nan 0.1000 -0.0001
## 400 0.0370 nan 0.1000 -0.0001
## 420 0.0333 nan 0.1000 -0.0002
## 440 0.0294 nan 0.1000 -0.0001
## 460 0.0260 nan 0.1000 -0.0000
## 480 0.0229 nan 0.1000 -0.0001
## 500 0.0206 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0003
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3044 nan 0.0010 0.0003
## 40 1.2881 nan 0.0010 0.0003
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2417 nan 0.0010 0.0003
## 120 1.2275 nan 0.0010 0.0003
## 140 1.2137 nan 0.0010 0.0003
## 160 1.2002 nan 0.0010 0.0003
## 180 1.1876 nan 0.0010 0.0003
## 200 1.1752 nan 0.0010 0.0002
## 220 1.1629 nan 0.0010 0.0003
## 240 1.1507 nan 0.0010 0.0003
## 260 1.1392 nan 0.0010 0.0003
## 280 1.1278 nan 0.0010 0.0002
## 300 1.1172 nan 0.0010 0.0002
## 320 1.1065 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0860 nan 0.0010 0.0002
## 380 1.0762 nan 0.0010 0.0002
## 400 1.0667 nan 0.0010 0.0002
## 420 1.0573 nan 0.0010 0.0002
## 440 1.0482 nan 0.0010 0.0002
## 460 1.0394 nan 0.0010 0.0002
## 480 1.0310 nan 0.0010 0.0001
## 500 1.0228 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0003
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0003
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2877 nan 0.0010 0.0003
## 60 1.2719 nan 0.0010 0.0004
## 80 1.2566 nan 0.0010 0.0004
## 100 1.2416 nan 0.0010 0.0003
## 120 1.2276 nan 0.0010 0.0003
## 140 1.2137 nan 0.0010 0.0003
## 160 1.2003 nan 0.0010 0.0003
## 180 1.1872 nan 0.0010 0.0003
## 200 1.1743 nan 0.0010 0.0003
## 220 1.1619 nan 0.0010 0.0003
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1386 nan 0.0010 0.0003
## 280 1.1274 nan 0.0010 0.0003
## 300 1.1164 nan 0.0010 0.0003
## 320 1.1061 nan 0.0010 0.0002
## 340 1.0960 nan 0.0010 0.0002
## 360 1.0858 nan 0.0010 0.0003
## 380 1.0759 nan 0.0010 0.0002
## 400 1.0663 nan 0.0010 0.0002
## 420 1.0571 nan 0.0010 0.0002
## 440 1.0481 nan 0.0010 0.0002
## 460 1.0393 nan 0.0010 0.0002
## 480 1.0307 nan 0.0010 0.0002
## 500 1.0221 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3196 nan 0.0010 0.0003
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0003
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2727 nan 0.0010 0.0003
## 80 1.2576 nan 0.0010 0.0003
## 100 1.2428 nan 0.0010 0.0003
## 120 1.2288 nan 0.0010 0.0003
## 140 1.2149 nan 0.0010 0.0003
## 160 1.2015 nan 0.0010 0.0003
## 180 1.1883 nan 0.0010 0.0003
## 200 1.1761 nan 0.0010 0.0003
## 220 1.1638 nan 0.0010 0.0003
## 240 1.1521 nan 0.0010 0.0003
## 260 1.1405 nan 0.0010 0.0002
## 280 1.1294 nan 0.0010 0.0003
## 300 1.1187 nan 0.0010 0.0002
## 320 1.1081 nan 0.0010 0.0002
## 340 1.0979 nan 0.0010 0.0003
## 360 1.0881 nan 0.0010 0.0002
## 380 1.0783 nan 0.0010 0.0002
## 400 1.0690 nan 0.0010 0.0002
## 420 1.0598 nan 0.0010 0.0002
## 440 1.0509 nan 0.0010 0.0002
## 460 1.0420 nan 0.0010 0.0002
## 480 1.0335 nan 0.0010 0.0002
## 500 1.0253 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0003
## 80 1.2521 nan 0.0010 0.0003
## 100 1.2361 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1907 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
## 200 1.1636 nan 0.0010 0.0003
## 220 1.1503 nan 0.0010 0.0003
## 240 1.1374 nan 0.0010 0.0002
## 260 1.1252 nan 0.0010 0.0002
## 280 1.1132 nan 0.0010 0.0003
## 300 1.1015 nan 0.0010 0.0003
## 320 1.0903 nan 0.0010 0.0002
## 340 1.0795 nan 0.0010 0.0002
## 360 1.0688 nan 0.0010 0.0002
## 380 1.0584 nan 0.0010 0.0002
## 400 1.0485 nan 0.0010 0.0002
## 420 1.0386 nan 0.0010 0.0002
## 440 1.0292 nan 0.0010 0.0002
## 460 1.0199 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2362 nan 0.0010 0.0004
## 120 1.2211 nan 0.0010 0.0004
## 140 1.2064 nan 0.0010 0.0003
## 160 1.1919 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1641 nan 0.0010 0.0003
## 220 1.1510 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0003
## 260 1.1260 nan 0.0010 0.0003
## 280 1.1141 nan 0.0010 0.0003
## 300 1.1024 nan 0.0010 0.0003
## 320 1.0912 nan 0.0010 0.0002
## 340 1.0801 nan 0.0010 0.0002
## 360 1.0695 nan 0.0010 0.0002
## 380 1.0592 nan 0.0010 0.0002
## 400 1.0493 nan 0.0010 0.0002
## 420 1.0396 nan 0.0010 0.0002
## 440 1.0302 nan 0.0010 0.0002
## 460 1.0210 nan 0.0010 0.0002
## 480 1.0120 nan 0.0010 0.0002
## 500 1.0032 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0005
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0003
## 60 1.2694 nan 0.0010 0.0004
## 80 1.2528 nan 0.0010 0.0004
## 100 1.2374 nan 0.0010 0.0004
## 120 1.2225 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1938 nan 0.0010 0.0003
## 180 1.1802 nan 0.0010 0.0003
## 200 1.1671 nan 0.0010 0.0003
## 220 1.1542 nan 0.0010 0.0003
## 240 1.1416 nan 0.0010 0.0003
## 260 1.1296 nan 0.0010 0.0003
## 280 1.1178 nan 0.0010 0.0002
## 300 1.1065 nan 0.0010 0.0002
## 320 1.0955 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0002
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0638 nan 0.0010 0.0003
## 400 1.0540 nan 0.0010 0.0002
## 420 1.0442 nan 0.0010 0.0002
## 440 1.0349 nan 0.0010 0.0002
## 460 1.0257 nan 0.0010 0.0002
## 480 1.0169 nan 0.0010 0.0001
## 500 1.0081 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2660 nan 0.0010 0.0004
## 80 1.2487 nan 0.0010 0.0004
## 100 1.2321 nan 0.0010 0.0004
## 120 1.2159 nan 0.0010 0.0004
## 140 1.2001 nan 0.0010 0.0003
## 160 1.1848 nan 0.0010 0.0003
## 180 1.1703 nan 0.0010 0.0003
## 200 1.1565 nan 0.0010 0.0003
## 220 1.1432 nan 0.0010 0.0002
## 240 1.1300 nan 0.0010 0.0003
## 260 1.1170 nan 0.0010 0.0003
## 280 1.1048 nan 0.0010 0.0003
## 300 1.0927 nan 0.0010 0.0003
## 320 1.0811 nan 0.0010 0.0003
## 340 1.0697 nan 0.0010 0.0002
## 360 1.0587 nan 0.0010 0.0003
## 380 1.0480 nan 0.0010 0.0002
## 400 1.0375 nan 0.0010 0.0002
## 420 1.0273 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0074 nan 0.0010 0.0002
## 480 0.9981 nan 0.0010 0.0002
## 500 0.9888 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2488 nan 0.0010 0.0004
## 100 1.2322 nan 0.0010 0.0004
## 120 1.2163 nan 0.0010 0.0004
## 140 1.2009 nan 0.0010 0.0003
## 160 1.1859 nan 0.0010 0.0004
## 180 1.1717 nan 0.0010 0.0003
## 200 1.1575 nan 0.0010 0.0003
## 220 1.1442 nan 0.0010 0.0003
## 240 1.1310 nan 0.0010 0.0003
## 260 1.1183 nan 0.0010 0.0003
## 280 1.1061 nan 0.0010 0.0003
## 300 1.0943 nan 0.0010 0.0003
## 320 1.0826 nan 0.0010 0.0002
## 340 1.0711 nan 0.0010 0.0002
## 360 1.0600 nan 0.0010 0.0002
## 380 1.0492 nan 0.0010 0.0002
## 400 1.0387 nan 0.0010 0.0002
## 420 1.0287 nan 0.0010 0.0002
## 440 1.0187 nan 0.0010 0.0002
## 460 1.0091 nan 0.0010 0.0002
## 480 0.9994 nan 0.0010 0.0002
## 500 0.9904 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0005
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2344 nan 0.0010 0.0004
## 120 1.2185 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1887 nan 0.0010 0.0002
## 180 1.1744 nan 0.0010 0.0004
## 200 1.1603 nan 0.0010 0.0003
## 220 1.1469 nan 0.0010 0.0003
## 240 1.1337 nan 0.0010 0.0003
## 260 1.1215 nan 0.0010 0.0002
## 280 1.1095 nan 0.0010 0.0003
## 300 1.0980 nan 0.0010 0.0003
## 320 1.0866 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0002
## 360 1.0645 nan 0.0010 0.0002
## 380 1.0538 nan 0.0010 0.0002
## 400 1.0438 nan 0.0010 0.0002
## 420 1.0338 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0145 nan 0.0010 0.0002
## 480 1.0051 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0036
## 2 1.3043 nan 0.0100 0.0038
## 3 1.2965 nan 0.0100 0.0037
## 4 1.2880 nan 0.0100 0.0040
## 5 1.2797 nan 0.0100 0.0034
## 6 1.2715 nan 0.0100 0.0037
## 7 1.2641 nan 0.0100 0.0032
## 8 1.2567 nan 0.0100 0.0032
## 9 1.2483 nan 0.0100 0.0040
## 10 1.2410 nan 0.0100 0.0034
## 20 1.1743 nan 0.0100 0.0029
## 40 1.0676 nan 0.0100 0.0020
## 60 0.9848 nan 0.0100 0.0016
## 80 0.9201 nan 0.0100 0.0009
## 100 0.8681 nan 0.0100 0.0008
## 120 0.8241 nan 0.0100 0.0008
## 140 0.7877 nan 0.0100 0.0004
## 160 0.7589 nan 0.0100 0.0003
## 180 0.7327 nan 0.0100 0.0004
## 200 0.7108 nan 0.0100 0.0003
## 220 0.6915 nan 0.0100 0.0002
## 240 0.6733 nan 0.0100 0.0002
## 260 0.6579 nan 0.0100 -0.0000
## 280 0.6437 nan 0.0100 0.0000
## 300 0.6294 nan 0.0100 -0.0000
## 320 0.6175 nan 0.0100 0.0000
## 340 0.6059 nan 0.0100 -0.0000
## 360 0.5942 nan 0.0100 0.0001
## 380 0.5831 nan 0.0100 -0.0001
## 400 0.5727 nan 0.0100 0.0000
## 420 0.5636 nan 0.0100 -0.0001
## 440 0.5550 nan 0.0100 -0.0001
## 460 0.5456 nan 0.0100 -0.0002
## 480 0.5364 nan 0.0100 -0.0000
## 500 0.5283 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3038 nan 0.0100 0.0040
## 3 1.2952 nan 0.0100 0.0039
## 4 1.2862 nan 0.0100 0.0039
## 5 1.2779 nan 0.0100 0.0039
## 6 1.2701 nan 0.0100 0.0036
## 7 1.2626 nan 0.0100 0.0036
## 8 1.2554 nan 0.0100 0.0037
## 9 1.2484 nan 0.0100 0.0033
## 10 1.2413 nan 0.0100 0.0031
## 20 1.1728 nan 0.0100 0.0028
## 40 1.0656 nan 0.0100 0.0022
## 60 0.9837 nan 0.0100 0.0014
## 80 0.9212 nan 0.0100 0.0010
## 100 0.8692 nan 0.0100 0.0009
## 120 0.8254 nan 0.0100 0.0005
## 140 0.7913 nan 0.0100 0.0005
## 160 0.7616 nan 0.0100 0.0003
## 180 0.7357 nan 0.0100 0.0002
## 200 0.7144 nan 0.0100 0.0002
## 220 0.6945 nan 0.0100 0.0003
## 240 0.6772 nan 0.0100 0.0000
## 260 0.6614 nan 0.0100 0.0001
## 280 0.6469 nan 0.0100 0.0003
## 300 0.6330 nan 0.0100 0.0001
## 320 0.6201 nan 0.0100 0.0001
## 340 0.6074 nan 0.0100 0.0001
## 360 0.5968 nan 0.0100 -0.0001
## 380 0.5868 nan 0.0100 0.0001
## 400 0.5779 nan 0.0100 -0.0002
## 420 0.5682 nan 0.0100 0.0000
## 440 0.5583 nan 0.0100 -0.0001
## 460 0.5498 nan 0.0100 0.0000
## 480 0.5411 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0044
## 2 1.3046 nan 0.0100 0.0032
## 3 1.2964 nan 0.0100 0.0032
## 4 1.2894 nan 0.0100 0.0032
## 5 1.2816 nan 0.0100 0.0035
## 6 1.2739 nan 0.0100 0.0037
## 7 1.2664 nan 0.0100 0.0032
## 8 1.2587 nan 0.0100 0.0037
## 9 1.2519 nan 0.0100 0.0032
## 10 1.2443 nan 0.0100 0.0034
## 20 1.1771 nan 0.0100 0.0028
## 40 1.0681 nan 0.0100 0.0016
## 60 0.9860 nan 0.0100 0.0016
## 80 0.9215 nan 0.0100 0.0015
## 100 0.8694 nan 0.0100 0.0008
## 120 0.8269 nan 0.0100 0.0008
## 140 0.7922 nan 0.0100 0.0005
## 160 0.7633 nan 0.0100 0.0004
## 180 0.7372 nan 0.0100 0.0002
## 200 0.7129 nan 0.0100 0.0004
## 220 0.6939 nan 0.0100 0.0002
## 240 0.6763 nan 0.0100 0.0002
## 260 0.6600 nan 0.0100 0.0001
## 280 0.6461 nan 0.0100 0.0000
## 300 0.6329 nan 0.0100 -0.0000
## 320 0.6206 nan 0.0100 0.0001
## 340 0.6094 nan 0.0100 -0.0002
## 360 0.5998 nan 0.0100 -0.0001
## 380 0.5903 nan 0.0100 -0.0001
## 400 0.5812 nan 0.0100 -0.0001
## 420 0.5719 nan 0.0100 0.0000
## 440 0.5634 nan 0.0100 -0.0001
## 460 0.5549 nan 0.0100 0.0001
## 480 0.5470 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3030 nan 0.0100 0.0045
## 3 1.2942 nan 0.0100 0.0040
## 4 1.2843 nan 0.0100 0.0040
## 5 1.2755 nan 0.0100 0.0037
## 6 1.2678 nan 0.0100 0.0032
## 7 1.2593 nan 0.0100 0.0036
## 8 1.2509 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0036
## 10 1.2358 nan 0.0100 0.0037
## 20 1.1614 nan 0.0100 0.0035
## 40 1.0484 nan 0.0100 0.0022
## 60 0.9598 nan 0.0100 0.0015
## 80 0.8928 nan 0.0100 0.0014
## 100 0.8391 nan 0.0100 0.0008
## 120 0.7932 nan 0.0100 0.0008
## 140 0.7565 nan 0.0100 0.0004
## 160 0.7264 nan 0.0100 0.0004
## 180 0.6991 nan 0.0100 0.0003
## 200 0.6760 nan 0.0100 0.0004
## 220 0.6546 nan 0.0100 0.0002
## 240 0.6358 nan 0.0100 0.0000
## 260 0.6181 nan 0.0100 0.0001
## 280 0.6025 nan 0.0100 -0.0001
## 300 0.5877 nan 0.0100 0.0001
## 320 0.5743 nan 0.0100 0.0002
## 340 0.5621 nan 0.0100 -0.0001
## 360 0.5490 nan 0.0100 0.0001
## 380 0.5367 nan 0.0100 0.0001
## 400 0.5252 nan 0.0100 -0.0001
## 420 0.5144 nan 0.0100 -0.0001
## 440 0.5033 nan 0.0100 0.0000
## 460 0.4939 nan 0.0100 -0.0001
## 480 0.4845 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0045
## 2 1.3028 nan 0.0100 0.0043
## 3 1.2949 nan 0.0100 0.0038
## 4 1.2857 nan 0.0100 0.0045
## 5 1.2778 nan 0.0100 0.0039
## 6 1.2702 nan 0.0100 0.0033
## 7 1.2616 nan 0.0100 0.0039
## 8 1.2531 nan 0.0100 0.0035
## 9 1.2456 nan 0.0100 0.0032
## 10 1.2370 nan 0.0100 0.0035
## 20 1.1643 nan 0.0100 0.0025
## 40 1.0497 nan 0.0100 0.0022
## 60 0.9622 nan 0.0100 0.0016
## 80 0.8935 nan 0.0100 0.0010
## 100 0.8390 nan 0.0100 0.0010
## 120 0.7947 nan 0.0100 0.0008
## 140 0.7577 nan 0.0100 0.0006
## 160 0.7267 nan 0.0100 0.0003
## 180 0.7005 nan 0.0100 0.0002
## 200 0.6763 nan 0.0100 0.0003
## 220 0.6556 nan 0.0100 0.0003
## 240 0.6364 nan 0.0100 0.0001
## 260 0.6203 nan 0.0100 0.0003
## 280 0.6040 nan 0.0100 0.0000
## 300 0.5900 nan 0.0100 0.0001
## 320 0.5762 nan 0.0100 -0.0001
## 340 0.5636 nan 0.0100 -0.0000
## 360 0.5513 nan 0.0100 0.0000
## 380 0.5399 nan 0.0100 -0.0001
## 400 0.5287 nan 0.0100 -0.0000
## 420 0.5182 nan 0.0100 -0.0000
## 440 0.5082 nan 0.0100 -0.0001
## 460 0.4987 nan 0.0100 0.0000
## 480 0.4899 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0045
## 2 1.3019 nan 0.0100 0.0045
## 3 1.2925 nan 0.0100 0.0041
## 4 1.2833 nan 0.0100 0.0042
## 5 1.2749 nan 0.0100 0.0038
## 6 1.2671 nan 0.0100 0.0037
## 7 1.2588 nan 0.0100 0.0034
## 8 1.2511 nan 0.0100 0.0035
## 9 1.2438 nan 0.0100 0.0032
## 10 1.2363 nan 0.0100 0.0032
## 20 1.1650 nan 0.0100 0.0032
## 40 1.0524 nan 0.0100 0.0019
## 60 0.9662 nan 0.0100 0.0017
## 80 0.8994 nan 0.0100 0.0012
## 100 0.8446 nan 0.0100 0.0010
## 120 0.7999 nan 0.0100 0.0006
## 140 0.7637 nan 0.0100 0.0005
## 160 0.7333 nan 0.0100 0.0005
## 180 0.7072 nan 0.0100 0.0003
## 200 0.6844 nan 0.0100 0.0002
## 220 0.6637 nan 0.0100 0.0002
## 240 0.6435 nan 0.0100 0.0001
## 260 0.6270 nan 0.0100 0.0002
## 280 0.6127 nan 0.0100 0.0001
## 300 0.5982 nan 0.0100 -0.0001
## 320 0.5849 nan 0.0100 0.0001
## 340 0.5713 nan 0.0100 -0.0001
## 360 0.5592 nan 0.0100 -0.0001
## 380 0.5485 nan 0.0100 -0.0001
## 400 0.5370 nan 0.0100 0.0001
## 420 0.5280 nan 0.0100 -0.0000
## 440 0.5181 nan 0.0100 0.0000
## 460 0.5093 nan 0.0100 -0.0000
## 480 0.4997 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0047
## 2 1.3013 nan 0.0100 0.0045
## 3 1.2932 nan 0.0100 0.0035
## 4 1.2838 nan 0.0100 0.0045
## 5 1.2754 nan 0.0100 0.0035
## 6 1.2657 nan 0.0100 0.0047
## 7 1.2570 nan 0.0100 0.0039
## 8 1.2489 nan 0.0100 0.0035
## 9 1.2413 nan 0.0100 0.0036
## 10 1.2336 nan 0.0100 0.0032
## 20 1.1599 nan 0.0100 0.0027
## 40 1.0382 nan 0.0100 0.0022
## 60 0.9469 nan 0.0100 0.0017
## 80 0.8752 nan 0.0100 0.0015
## 100 0.8192 nan 0.0100 0.0009
## 120 0.7730 nan 0.0100 0.0006
## 140 0.7325 nan 0.0100 0.0006
## 160 0.6996 nan 0.0100 0.0006
## 180 0.6705 nan 0.0100 0.0004
## 200 0.6442 nan 0.0100 0.0003
## 220 0.6215 nan 0.0100 0.0000
## 240 0.6020 nan 0.0100 0.0001
## 260 0.5830 nan 0.0100 -0.0000
## 280 0.5658 nan 0.0100 -0.0002
## 300 0.5506 nan 0.0100 0.0001
## 320 0.5350 nan 0.0100 0.0002
## 340 0.5211 nan 0.0100 -0.0000
## 360 0.5081 nan 0.0100 0.0000
## 380 0.4948 nan 0.0100 -0.0000
## 400 0.4831 nan 0.0100 -0.0000
## 420 0.4717 nan 0.0100 0.0000
## 440 0.4607 nan 0.0100 0.0000
## 460 0.4507 nan 0.0100 -0.0001
## 480 0.4407 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0048
## 2 1.3005 nan 0.0100 0.0048
## 3 1.2913 nan 0.0100 0.0039
## 4 1.2819 nan 0.0100 0.0043
## 5 1.2734 nan 0.0100 0.0043
## 6 1.2640 nan 0.0100 0.0039
## 7 1.2549 nan 0.0100 0.0037
## 8 1.2461 nan 0.0100 0.0038
## 9 1.2379 nan 0.0100 0.0036
## 10 1.2301 nan 0.0100 0.0035
## 20 1.1560 nan 0.0100 0.0034
## 40 1.0385 nan 0.0100 0.0022
## 60 0.9476 nan 0.0100 0.0017
## 80 0.8764 nan 0.0100 0.0014
## 100 0.8208 nan 0.0100 0.0008
## 120 0.7747 nan 0.0100 0.0007
## 140 0.7360 nan 0.0100 0.0004
## 160 0.7032 nan 0.0100 0.0005
## 180 0.6747 nan 0.0100 0.0004
## 200 0.6506 nan 0.0100 0.0002
## 220 0.6271 nan 0.0100 0.0004
## 240 0.6070 nan 0.0100 0.0001
## 260 0.5893 nan 0.0100 0.0001
## 280 0.5725 nan 0.0100 0.0001
## 300 0.5565 nan 0.0100 0.0000
## 320 0.5415 nan 0.0100 0.0000
## 340 0.5280 nan 0.0100 -0.0000
## 360 0.5148 nan 0.0100 0.0000
## 380 0.5035 nan 0.0100 0.0001
## 400 0.4914 nan 0.0100 0.0001
## 420 0.4796 nan 0.0100 0.0000
## 440 0.4688 nan 0.0100 -0.0001
## 460 0.4580 nan 0.0100 -0.0000
## 480 0.4482 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0047
## 2 1.3022 nan 0.0100 0.0042
## 3 1.2925 nan 0.0100 0.0046
## 4 1.2834 nan 0.0100 0.0040
## 5 1.2746 nan 0.0100 0.0040
## 6 1.2655 nan 0.0100 0.0040
## 7 1.2571 nan 0.0100 0.0038
## 8 1.2488 nan 0.0100 0.0035
## 9 1.2408 nan 0.0100 0.0040
## 10 1.2332 nan 0.0100 0.0035
## 20 1.1613 nan 0.0100 0.0028
## 40 1.0422 nan 0.0100 0.0022
## 60 0.9529 nan 0.0100 0.0017
## 80 0.8836 nan 0.0100 0.0010
## 100 0.8282 nan 0.0100 0.0009
## 120 0.7836 nan 0.0100 0.0005
## 140 0.7467 nan 0.0100 0.0005
## 160 0.7130 nan 0.0100 0.0004
## 180 0.6845 nan 0.0100 0.0006
## 200 0.6595 nan 0.0100 0.0003
## 220 0.6374 nan 0.0100 0.0000
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## 260 0.6004 nan 0.0100 0.0001
## 280 0.5832 nan 0.0100 0.0001
## 300 0.5660 nan 0.0100 -0.0000
## 320 0.5519 nan 0.0100 -0.0001
## 340 0.5378 nan 0.0100 0.0001
## 360 0.5252 nan 0.0100 -0.0000
## 380 0.5128 nan 0.0100 0.0000
## 400 0.5011 nan 0.0100 -0.0001
## 420 0.4898 nan 0.0100 -0.0001
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## 460 0.4693 nan 0.0100 0.0000
## 480 0.4590 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0394
## 2 1.1697 nan 0.1000 0.0295
## 3 1.1135 nan 0.1000 0.0238
## 4 1.0599 nan 0.1000 0.0250
## 5 1.0173 nan 0.1000 0.0202
## 6 0.9851 nan 0.1000 0.0122
## 7 0.9488 nan 0.1000 0.0130
## 8 0.9203 nan 0.1000 0.0122
## 9 0.8975 nan 0.1000 0.0073
## 10 0.8697 nan 0.1000 0.0102
## 20 0.7106 nan 0.1000 0.0014
## 40 0.5850 nan 0.1000 -0.0010
## 60 0.5100 nan 0.1000 -0.0005
## 80 0.4385 nan 0.1000 0.0015
## 100 0.3836 nan 0.1000 0.0001
## 120 0.3477 nan 0.1000 -0.0007
## 140 0.3090 nan 0.1000 -0.0007
## 160 0.2765 nan 0.1000 -0.0000
## 180 0.2488 nan 0.1000 0.0001
## 200 0.2228 nan 0.1000 -0.0005
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## 320 0.1276 nan 0.1000 -0.0006
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## 380 0.0961 nan 0.1000 -0.0001
## 400 0.0878 nan 0.1000 -0.0002
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## 460 0.0690 nan 0.1000 -0.0000
## 480 0.0641 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2380 nan 0.1000 0.0384
## 2 1.1715 nan 0.1000 0.0311
## 3 1.1129 nan 0.1000 0.0250
## 4 1.0559 nan 0.1000 0.0241
## 5 1.0116 nan 0.1000 0.0165
## 6 0.9746 nan 0.1000 0.0150
## 7 0.9405 nan 0.1000 0.0130
## 8 0.9096 nan 0.1000 0.0122
## 9 0.8853 nan 0.1000 0.0095
## 10 0.8607 nan 0.1000 0.0081
## 20 0.7045 nan 0.1000 0.0032
## 40 0.5829 nan 0.1000 -0.0017
## 60 0.5000 nan 0.1000 0.0013
## 80 0.4396 nan 0.1000 -0.0015
## 100 0.3911 nan 0.1000 -0.0005
## 120 0.3465 nan 0.1000 -0.0007
## 140 0.3068 nan 0.1000 -0.0001
## 160 0.2746 nan 0.1000 -0.0012
## 180 0.2480 nan 0.1000 -0.0006
## 200 0.2248 nan 0.1000 -0.0008
## 220 0.2033 nan 0.1000 0.0001
## 240 0.1825 nan 0.1000 -0.0004
## 260 0.1661 nan 0.1000 -0.0006
## 280 0.1524 nan 0.1000 -0.0001
## 300 0.1402 nan 0.1000 -0.0004
## 320 0.1280 nan 0.1000 -0.0004
## 340 0.1176 nan 0.1000 -0.0005
## 360 0.1078 nan 0.1000 -0.0002
## 380 0.0980 nan 0.1000 -0.0002
## 400 0.0902 nan 0.1000 -0.0003
## 420 0.0837 nan 0.1000 -0.0002
## 440 0.0771 nan 0.1000 -0.0004
## 460 0.0714 nan 0.1000 -0.0002
## 480 0.0658 nan 0.1000 -0.0001
## 500 0.0608 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2515 nan 0.1000 0.0322
## 2 1.1786 nan 0.1000 0.0322
## 3 1.1205 nan 0.1000 0.0236
## 4 1.0700 nan 0.1000 0.0204
## 5 1.0232 nan 0.1000 0.0195
## 6 0.9869 nan 0.1000 0.0160
## 7 0.9537 nan 0.1000 0.0137
## 8 0.9200 nan 0.1000 0.0125
## 9 0.8963 nan 0.1000 0.0114
## 10 0.8733 nan 0.1000 0.0080
## 20 0.7202 nan 0.1000 0.0021
## 40 0.5902 nan 0.1000 -0.0005
## 60 0.5128 nan 0.1000 -0.0012
## 80 0.4548 nan 0.1000 0.0004
## 100 0.3988 nan 0.1000 0.0001
## 120 0.3571 nan 0.1000 0.0002
## 140 0.3222 nan 0.1000 0.0003
## 160 0.2921 nan 0.1000 -0.0000
## 180 0.2610 nan 0.1000 -0.0008
## 200 0.2376 nan 0.1000 -0.0013
## 220 0.2164 nan 0.1000 -0.0010
## 240 0.1966 nan 0.1000 -0.0009
## 260 0.1782 nan 0.1000 -0.0004
## 280 0.1612 nan 0.1000 -0.0007
## 300 0.1488 nan 0.1000 -0.0001
## 320 0.1361 nan 0.1000 -0.0006
## 340 0.1256 nan 0.1000 -0.0007
## 360 0.1149 nan 0.1000 -0.0001
## 380 0.1053 nan 0.1000 -0.0003
## 400 0.0971 nan 0.1000 -0.0004
## 420 0.0892 nan 0.1000 -0.0004
## 440 0.0819 nan 0.1000 -0.0004
## 460 0.0766 nan 0.1000 -0.0004
## 480 0.0707 nan 0.1000 -0.0002
## 500 0.0654 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2283 nan 0.1000 0.0425
## 2 1.1602 nan 0.1000 0.0324
## 3 1.0988 nan 0.1000 0.0244
## 4 1.0405 nan 0.1000 0.0269
## 5 0.9900 nan 0.1000 0.0153
## 6 0.9509 nan 0.1000 0.0168
## 7 0.9192 nan 0.1000 0.0110
## 8 0.8927 nan 0.1000 0.0091
## 9 0.8634 nan 0.1000 0.0122
## 10 0.8385 nan 0.1000 0.0071
## 20 0.6704 nan 0.1000 0.0038
## 40 0.5280 nan 0.1000 -0.0016
## 60 0.4374 nan 0.1000 0.0000
## 80 0.3716 nan 0.1000 -0.0009
## 100 0.3165 nan 0.1000 -0.0004
## 120 0.2692 nan 0.1000 -0.0018
## 140 0.2315 nan 0.1000 -0.0001
## 160 0.1996 nan 0.1000 -0.0006
## 180 0.1756 nan 0.1000 -0.0003
## 200 0.1527 nan 0.1000 -0.0003
## 220 0.1356 nan 0.1000 -0.0003
## 240 0.1205 nan 0.1000 -0.0000
## 260 0.1084 nan 0.1000 -0.0002
## 280 0.0972 nan 0.1000 -0.0002
## 300 0.0874 nan 0.1000 -0.0001
## 320 0.0784 nan 0.1000 -0.0002
## 340 0.0702 nan 0.1000 -0.0001
## 360 0.0632 nan 0.1000 -0.0001
## 380 0.0566 nan 0.1000 -0.0001
## 400 0.0501 nan 0.1000 -0.0001
## 420 0.0451 nan 0.1000 -0.0001
## 440 0.0408 nan 0.1000 -0.0001
## 460 0.0373 nan 0.1000 -0.0001
## 480 0.0334 nan 0.1000 -0.0000
## 500 0.0304 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.1000 0.0440
## 2 1.1548 nan 0.1000 0.0302
## 3 1.0927 nan 0.1000 0.0259
## 4 1.0366 nan 0.1000 0.0212
## 5 0.9925 nan 0.1000 0.0195
## 6 0.9550 nan 0.1000 0.0171
## 7 0.9190 nan 0.1000 0.0155
## 8 0.8867 nan 0.1000 0.0116
## 9 0.8576 nan 0.1000 0.0110
## 10 0.8326 nan 0.1000 0.0090
## 20 0.6773 nan 0.1000 0.0026
## 40 0.5369 nan 0.1000 -0.0012
## 60 0.4528 nan 0.1000 0.0000
## 80 0.3841 nan 0.1000 -0.0008
## 100 0.3242 nan 0.1000 0.0004
## 120 0.2822 nan 0.1000 -0.0017
## 140 0.2462 nan 0.1000 -0.0009
## 160 0.2162 nan 0.1000 -0.0005
## 180 0.1890 nan 0.1000 -0.0004
## 200 0.1653 nan 0.1000 -0.0001
## 220 0.1458 nan 0.1000 -0.0007
## 240 0.1283 nan 0.1000 0.0001
## 260 0.1149 nan 0.1000 -0.0004
## 280 0.1028 nan 0.1000 -0.0003
## 300 0.0933 nan 0.1000 -0.0005
## 320 0.0833 nan 0.1000 -0.0003
## 340 0.0751 nan 0.1000 -0.0002
## 360 0.0663 nan 0.1000 -0.0002
## 380 0.0592 nan 0.1000 -0.0002
## 400 0.0533 nan 0.1000 -0.0001
## 420 0.0478 nan 0.1000 -0.0001
## 440 0.0439 nan 0.1000 -0.0003
## 460 0.0391 nan 0.1000 -0.0002
## 480 0.0349 nan 0.1000 -0.0001
## 500 0.0315 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0395
## 2 1.1588 nan 0.1000 0.0318
## 3 1.0987 nan 0.1000 0.0264
## 4 1.0490 nan 0.1000 0.0215
## 5 1.0089 nan 0.1000 0.0173
## 6 0.9698 nan 0.1000 0.0162
## 7 0.9311 nan 0.1000 0.0158
## 8 0.8981 nan 0.1000 0.0137
## 9 0.8698 nan 0.1000 0.0097
## 10 0.8453 nan 0.1000 0.0102
## 20 0.6898 nan 0.1000 0.0014
## 40 0.5395 nan 0.1000 0.0004
## 60 0.4526 nan 0.1000 -0.0000
## 80 0.3807 nan 0.1000 -0.0008
## 100 0.3286 nan 0.1000 -0.0007
## 120 0.2910 nan 0.1000 -0.0001
## 140 0.2515 nan 0.1000 -0.0006
## 160 0.2217 nan 0.1000 -0.0002
## 180 0.1971 nan 0.1000 -0.0009
## 200 0.1737 nan 0.1000 -0.0007
## 220 0.1535 nan 0.1000 -0.0007
## 240 0.1359 nan 0.1000 -0.0010
## 260 0.1219 nan 0.1000 -0.0006
## 280 0.1080 nan 0.1000 -0.0003
## 300 0.0981 nan 0.1000 -0.0005
## 320 0.0890 nan 0.1000 -0.0004
## 340 0.0806 nan 0.1000 -0.0002
## 360 0.0730 nan 0.1000 -0.0001
## 380 0.0660 nan 0.1000 -0.0003
## 400 0.0596 nan 0.1000 -0.0001
## 420 0.0538 nan 0.1000 -0.0001
## 440 0.0482 nan 0.1000 -0.0001
## 460 0.0432 nan 0.1000 -0.0001
## 480 0.0389 nan 0.1000 -0.0001
## 500 0.0350 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2234 nan 0.1000 0.0478
## 2 1.1554 nan 0.1000 0.0303
## 3 1.0933 nan 0.1000 0.0298
## 4 1.0376 nan 0.1000 0.0242
## 5 0.9835 nan 0.1000 0.0229
## 6 0.9388 nan 0.1000 0.0189
## 7 0.9036 nan 0.1000 0.0141
## 8 0.8730 nan 0.1000 0.0102
## 9 0.8409 nan 0.1000 0.0132
## 10 0.8129 nan 0.1000 0.0094
## 20 0.6465 nan 0.1000 0.0030
## 40 0.4794 nan 0.1000 0.0004
## 60 0.3904 nan 0.1000 -0.0010
## 80 0.3202 nan 0.1000 -0.0004
## 100 0.2656 nan 0.1000 -0.0003
## 120 0.2211 nan 0.1000 -0.0008
## 140 0.1881 nan 0.1000 -0.0004
## 160 0.1634 nan 0.1000 -0.0003
## 180 0.1420 nan 0.1000 -0.0005
## 200 0.1209 nan 0.1000 -0.0000
## 220 0.1031 nan 0.1000 -0.0001
## 240 0.0889 nan 0.1000 -0.0000
## 260 0.0766 nan 0.1000 -0.0002
## 280 0.0677 nan 0.1000 -0.0002
## 300 0.0592 nan 0.1000 -0.0004
## 320 0.0524 nan 0.1000 -0.0001
## 340 0.0452 nan 0.1000 -0.0003
## 360 0.0399 nan 0.1000 -0.0000
## 380 0.0358 nan 0.1000 -0.0001
## 400 0.0316 nan 0.1000 -0.0001
## 420 0.0282 nan 0.1000 -0.0000
## 440 0.0249 nan 0.1000 -0.0001
## 460 0.0224 nan 0.1000 -0.0000
## 480 0.0197 nan 0.1000 -0.0001
## 500 0.0174 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2292 nan 0.1000 0.0408
## 2 1.1470 nan 0.1000 0.0381
## 3 1.0858 nan 0.1000 0.0260
## 4 1.0336 nan 0.1000 0.0230
## 5 0.9890 nan 0.1000 0.0180
## 6 0.9505 nan 0.1000 0.0156
## 7 0.9115 nan 0.1000 0.0129
## 8 0.8763 nan 0.1000 0.0139
## 9 0.8489 nan 0.1000 0.0099
## 10 0.8214 nan 0.1000 0.0110
## 20 0.6611 nan 0.1000 0.0029
## 40 0.5005 nan 0.1000 -0.0016
## 60 0.4032 nan 0.1000 -0.0013
## 80 0.3344 nan 0.1000 -0.0011
## 100 0.2879 nan 0.1000 -0.0003
## 120 0.2415 nan 0.1000 -0.0002
## 140 0.2060 nan 0.1000 0.0001
## 160 0.1745 nan 0.1000 -0.0008
## 180 0.1481 nan 0.1000 -0.0008
## 200 0.1249 nan 0.1000 -0.0000
## 220 0.1074 nan 0.1000 -0.0000
## 240 0.0925 nan 0.1000 -0.0003
## 260 0.0790 nan 0.1000 -0.0002
## 280 0.0690 nan 0.1000 0.0000
## 300 0.0596 nan 0.1000 -0.0001
## 320 0.0518 nan 0.1000 -0.0000
## 340 0.0458 nan 0.1000 -0.0003
## 360 0.0407 nan 0.1000 -0.0002
## 380 0.0353 nan 0.1000 0.0001
## 400 0.0312 nan 0.1000 -0.0001
## 420 0.0275 nan 0.1000 -0.0001
## 440 0.0236 nan 0.1000 -0.0001
## 460 0.0207 nan 0.1000 -0.0001
## 480 0.0183 nan 0.1000 -0.0001
## 500 0.0162 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0367
## 2 1.1629 nan 0.1000 0.0351
## 3 1.1008 nan 0.1000 0.0252
## 4 1.0391 nan 0.1000 0.0281
## 5 0.9928 nan 0.1000 0.0202
## 6 0.9499 nan 0.1000 0.0188
## 7 0.9065 nan 0.1000 0.0157
## 8 0.8758 nan 0.1000 0.0106
## 9 0.8465 nan 0.1000 0.0106
## 10 0.8163 nan 0.1000 0.0122
## 20 0.6473 nan 0.1000 0.0032
## 40 0.4988 nan 0.1000 0.0005
## 60 0.4048 nan 0.1000 0.0001
## 80 0.3357 nan 0.1000 -0.0006
## 100 0.2834 nan 0.1000 -0.0001
## 120 0.2416 nan 0.1000 -0.0007
## 140 0.2043 nan 0.1000 -0.0006
## 160 0.1766 nan 0.1000 -0.0011
## 180 0.1526 nan 0.1000 -0.0007
## 200 0.1318 nan 0.1000 -0.0002
## 220 0.1145 nan 0.1000 -0.0005
## 240 0.1003 nan 0.1000 -0.0005
## 260 0.0878 nan 0.1000 -0.0006
## 280 0.0765 nan 0.1000 -0.0001
## 300 0.0675 nan 0.1000 -0.0001
## 320 0.0594 nan 0.1000 -0.0003
## 340 0.0525 nan 0.1000 -0.0001
## 360 0.0457 nan 0.1000 -0.0002
## 380 0.0404 nan 0.1000 -0.0001
## 400 0.0352 nan 0.1000 -0.0001
## 420 0.0312 nan 0.1000 -0.0002
## 440 0.0275 nan 0.1000 -0.0001
## 460 0.0243 nan 0.1000 -0.0001
## 480 0.0217 nan 0.1000 -0.0000
## 500 0.0190 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2537 nan 0.0010 0.0003
## 100 1.2381 nan 0.0010 0.0003
## 120 1.2229 nan 0.0010 0.0003
## 140 1.2087 nan 0.0010 0.0003
## 160 1.1945 nan 0.0010 0.0003
## 180 1.1808 nan 0.0010 0.0003
## 200 1.1677 nan 0.0010 0.0003
## 220 1.1546 nan 0.0010 0.0003
## 240 1.1421 nan 0.0010 0.0003
## 260 1.1299 nan 0.0010 0.0003
## 280 1.1181 nan 0.0010 0.0003
## 300 1.1069 nan 0.0010 0.0002
## 320 1.0957 nan 0.0010 0.0002
## 340 1.0849 nan 0.0010 0.0002
## 360 1.0743 nan 0.0010 0.0002
## 380 1.0639 nan 0.0010 0.0002
## 400 1.0538 nan 0.0010 0.0002
## 420 1.0441 nan 0.0010 0.0002
## 440 1.0347 nan 0.0010 0.0002
## 460 1.0256 nan 0.0010 0.0002
## 480 1.0167 nan 0.0010 0.0002
## 500 1.0080 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2539 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2091 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0004
## 180 1.1815 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1427 nan 0.0010 0.0003
## 260 1.1307 nan 0.0010 0.0002
## 280 1.1191 nan 0.0010 0.0003
## 300 1.1075 nan 0.0010 0.0003
## 320 1.0967 nan 0.0010 0.0003
## 340 1.0858 nan 0.0010 0.0002
## 360 1.0752 nan 0.0010 0.0002
## 380 1.0651 nan 0.0010 0.0002
## 400 1.0552 nan 0.0010 0.0002
## 420 1.0456 nan 0.0010 0.0002
## 440 1.0362 nan 0.0010 0.0002
## 460 1.0269 nan 0.0010 0.0002
## 480 1.0181 nan 0.0010 0.0002
## 500 1.0093 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2536 nan 0.0010 0.0003
## 100 1.2380 nan 0.0010 0.0004
## 120 1.2231 nan 0.0010 0.0003
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1948 nan 0.0010 0.0003
## 180 1.1814 nan 0.0010 0.0003
## 200 1.1686 nan 0.0010 0.0003
## 220 1.1558 nan 0.0010 0.0003
## 240 1.1436 nan 0.0010 0.0002
## 260 1.1315 nan 0.0010 0.0003
## 280 1.1199 nan 0.0010 0.0002
## 300 1.1087 nan 0.0010 0.0002
## 320 1.0976 nan 0.0010 0.0002
## 340 1.0869 nan 0.0010 0.0002
## 360 1.0765 nan 0.0010 0.0002
## 380 1.0664 nan 0.0010 0.0002
## 400 1.0567 nan 0.0010 0.0002
## 420 1.0470 nan 0.0010 0.0002
## 440 1.0379 nan 0.0010 0.0002
## 460 1.0288 nan 0.0010 0.0002
## 480 1.0202 nan 0.0010 0.0002
## 500 1.0116 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0005
## 60 1.2661 nan 0.0010 0.0004
## 80 1.2489 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0004
## 120 1.2165 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
## 160 1.1855 nan 0.0010 0.0004
## 180 1.1709 nan 0.0010 0.0004
## 200 1.1570 nan 0.0010 0.0003
## 220 1.1432 nan 0.0010 0.0003
## 240 1.1301 nan 0.0010 0.0002
## 260 1.1173 nan 0.0010 0.0003
## 280 1.1049 nan 0.0010 0.0003
## 300 1.0928 nan 0.0010 0.0003
## 320 1.0814 nan 0.0010 0.0003
## 340 1.0702 nan 0.0010 0.0002
## 360 1.0592 nan 0.0010 0.0002
## 380 1.0487 nan 0.0010 0.0002
## 400 1.0384 nan 0.0010 0.0002
## 420 1.0283 nan 0.0010 0.0002
## 440 1.0182 nan 0.0010 0.0002
## 460 1.0088 nan 0.0010 0.0002
## 480 0.9997 nan 0.0010 0.0002
## 500 0.9904 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2660 nan 0.0010 0.0004
## 80 1.2490 nan 0.0010 0.0004
## 100 1.2323 nan 0.0010 0.0004
## 120 1.2163 nan 0.0010 0.0004
## 140 1.2012 nan 0.0010 0.0003
## 160 1.1860 nan 0.0010 0.0004
## 180 1.1713 nan 0.0010 0.0003
## 200 1.1574 nan 0.0010 0.0003
## 220 1.1439 nan 0.0010 0.0003
## 240 1.1310 nan 0.0010 0.0003
## 260 1.1182 nan 0.0010 0.0002
## 280 1.1056 nan 0.0010 0.0003
## 300 1.0934 nan 0.0010 0.0003
## 320 1.0819 nan 0.0010 0.0003
## 340 1.0707 nan 0.0010 0.0002
## 360 1.0596 nan 0.0010 0.0003
## 380 1.0490 nan 0.0010 0.0002
## 400 1.0385 nan 0.0010 0.0002
## 420 1.0283 nan 0.0010 0.0002
## 440 1.0186 nan 0.0010 0.0002
## 460 1.0090 nan 0.0010 0.0002
## 480 0.9995 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0004
## 120 1.2179 nan 0.0010 0.0003
## 140 1.2028 nan 0.0010 0.0004
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0002
## 200 1.1598 nan 0.0010 0.0003
## 220 1.1459 nan 0.0010 0.0004
## 240 1.1328 nan 0.0010 0.0003
## 260 1.1197 nan 0.0010 0.0003
## 280 1.1074 nan 0.0010 0.0003
## 300 1.0955 nan 0.0010 0.0003
## 320 1.0837 nan 0.0010 0.0003
## 340 1.0724 nan 0.0010 0.0002
## 360 1.0614 nan 0.0010 0.0003
## 380 1.0507 nan 0.0010 0.0003
## 400 1.0403 nan 0.0010 0.0002
## 420 1.0304 nan 0.0010 0.0002
## 440 1.0207 nan 0.0010 0.0002
## 460 1.0112 nan 0.0010 0.0002
## 480 1.0019 nan 0.0010 0.0002
## 500 0.9928 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0005
## 4 1.3170 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3127 nan 0.0010 0.0005
## 9 1.3117 nan 0.0010 0.0004
## 10 1.3106 nan 0.0010 0.0005
## 20 1.3009 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2636 nan 0.0010 0.0004
## 80 1.2455 nan 0.0010 0.0004
## 100 1.2283 nan 0.0010 0.0004
## 120 1.2117 nan 0.0010 0.0004
## 140 1.1956 nan 0.0010 0.0004
## 160 1.1796 nan 0.0010 0.0003
## 180 1.1644 nan 0.0010 0.0003
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## 240 1.1217 nan 0.0010 0.0003
## 260 1.1085 nan 0.0010 0.0003
## 280 1.0955 nan 0.0010 0.0003
## 300 1.0831 nan 0.0010 0.0003
## 320 1.0708 nan 0.0010 0.0003
## 340 1.0591 nan 0.0010 0.0002
## 360 1.0475 nan 0.0010 0.0003
## 380 1.0366 nan 0.0010 0.0003
## 400 1.0259 nan 0.0010 0.0002
## 420 1.0152 nan 0.0010 0.0003
## 440 1.0048 nan 0.0010 0.0002
## 460 0.9949 nan 0.0010 0.0002
## 480 0.9851 nan 0.0010 0.0002
## 500 0.9757 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3152 nan 0.0010 0.0005
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3112 nan 0.0010 0.0005
## 20 1.3013 nan 0.0010 0.0005
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2640 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0003
## 100 1.2291 nan 0.0010 0.0004
## 120 1.2125 nan 0.0010 0.0004
## 140 1.1963 nan 0.0010 0.0004
## 160 1.1807 nan 0.0010 0.0003
## 180 1.1656 nan 0.0010 0.0003
## 200 1.1510 nan 0.0010 0.0003
## 220 1.1366 nan 0.0010 0.0003
## 240 1.1228 nan 0.0010 0.0003
## 260 1.1097 nan 0.0010 0.0003
## 280 1.0968 nan 0.0010 0.0003
## 300 1.0842 nan 0.0010 0.0003
## 320 1.0718 nan 0.0010 0.0003
## 340 1.0601 nan 0.0010 0.0003
## 360 1.0486 nan 0.0010 0.0002
## 380 1.0374 nan 0.0010 0.0002
## 400 1.0269 nan 0.0010 0.0002
## 420 1.0164 nan 0.0010 0.0002
## 440 1.0062 nan 0.0010 0.0002
## 460 0.9963 nan 0.0010 0.0002
## 480 0.9866 nan 0.0010 0.0002
## 500 0.9771 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2639 nan 0.0010 0.0004
## 80 1.2464 nan 0.0010 0.0004
## 100 1.2296 nan 0.0010 0.0004
## 120 1.2134 nan 0.0010 0.0004
## 140 1.1977 nan 0.0010 0.0004
## 160 1.1826 nan 0.0010 0.0003
## 180 1.1678 nan 0.0010 0.0003
## 200 1.1535 nan 0.0010 0.0003
## 220 1.1394 nan 0.0010 0.0003
## 240 1.1258 nan 0.0010 0.0003
## 260 1.1125 nan 0.0010 0.0003
## 280 1.0997 nan 0.0010 0.0003
## 300 1.0873 nan 0.0010 0.0003
## 320 1.0752 nan 0.0010 0.0003
## 340 1.0632 nan 0.0010 0.0003
## 360 1.0518 nan 0.0010 0.0002
## 380 1.0406 nan 0.0010 0.0002
## 400 1.0301 nan 0.0010 0.0002
## 420 1.0198 nan 0.0010 0.0002
## 440 1.0096 nan 0.0010 0.0002
## 460 0.9999 nan 0.0010 0.0002
## 480 0.9903 nan 0.0010 0.0002
## 500 0.9808 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3039 nan 0.0100 0.0036
## 3 1.2944 nan 0.0100 0.0039
## 4 1.2847 nan 0.0100 0.0040
## 5 1.2764 nan 0.0100 0.0039
## 6 1.2685 nan 0.0100 0.0037
## 7 1.2596 nan 0.0100 0.0039
## 8 1.2512 nan 0.0100 0.0035
## 9 1.2435 nan 0.0100 0.0037
## 10 1.2357 nan 0.0100 0.0034
## 20 1.1639 nan 0.0100 0.0030
## 40 1.0546 nan 0.0100 0.0020
## 60 0.9689 nan 0.0100 0.0012
## 80 0.9002 nan 0.0100 0.0013
## 100 0.8466 nan 0.0100 0.0010
## 120 0.8034 nan 0.0100 0.0008
## 140 0.7664 nan 0.0100 0.0005
## 160 0.7357 nan 0.0100 0.0002
## 180 0.7087 nan 0.0100 0.0002
## 200 0.6873 nan 0.0100 0.0001
## 220 0.6669 nan 0.0100 0.0000
## 240 0.6491 nan 0.0100 0.0001
## 260 0.6333 nan 0.0100 0.0001
## 280 0.6196 nan 0.0100 0.0002
## 300 0.6075 nan 0.0100 -0.0000
## 320 0.5956 nan 0.0100 -0.0000
## 340 0.5840 nan 0.0100 0.0000
## 360 0.5737 nan 0.0100 -0.0001
## 380 0.5641 nan 0.0100 0.0001
## 400 0.5549 nan 0.0100 0.0001
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## 440 0.5369 nan 0.0100 -0.0000
## 460 0.5285 nan 0.0100 -0.0001
## 480 0.5212 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0042
## 2 1.3035 nan 0.0100 0.0044
## 3 1.2953 nan 0.0100 0.0038
## 4 1.2862 nan 0.0100 0.0040
## 5 1.2776 nan 0.0100 0.0038
## 6 1.2698 nan 0.0100 0.0036
## 7 1.2620 nan 0.0100 0.0032
## 8 1.2535 nan 0.0100 0.0039
## 9 1.2457 nan 0.0100 0.0040
## 10 1.2384 nan 0.0100 0.0037
## 20 1.1690 nan 0.0100 0.0027
## 40 1.0570 nan 0.0100 0.0022
## 60 0.9708 nan 0.0100 0.0017
## 80 0.9043 nan 0.0100 0.0014
## 100 0.8499 nan 0.0100 0.0010
## 120 0.8059 nan 0.0100 0.0006
## 140 0.7686 nan 0.0100 0.0006
## 160 0.7380 nan 0.0100 0.0004
## 180 0.7126 nan 0.0100 0.0004
## 200 0.6909 nan 0.0100 0.0003
## 220 0.6717 nan 0.0100 0.0003
## 240 0.6551 nan 0.0100 -0.0001
## 260 0.6398 nan 0.0100 0.0002
## 280 0.6277 nan 0.0100 0.0001
## 300 0.6143 nan 0.0100 -0.0001
## 320 0.6023 nan 0.0100 -0.0001
## 340 0.5905 nan 0.0100 -0.0001
## 360 0.5800 nan 0.0100 -0.0001
## 380 0.5703 nan 0.0100 -0.0000
## 400 0.5614 nan 0.0100 -0.0001
## 420 0.5530 nan 0.0100 -0.0000
## 440 0.5443 nan 0.0100 -0.0001
## 460 0.5374 nan 0.0100 -0.0001
## 480 0.5296 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3038 nan 0.0100 0.0039
## 3 1.2953 nan 0.0100 0.0041
## 4 1.2863 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0037
## 6 1.2704 nan 0.0100 0.0033
## 7 1.2622 nan 0.0100 0.0036
## 8 1.2539 nan 0.0100 0.0039
## 9 1.2463 nan 0.0100 0.0033
## 10 1.2383 nan 0.0100 0.0039
## 20 1.1698 nan 0.0100 0.0029
## 40 1.0564 nan 0.0100 0.0019
## 60 0.9706 nan 0.0100 0.0016
## 80 0.9017 nan 0.0100 0.0008
## 100 0.8483 nan 0.0100 0.0008
## 120 0.8052 nan 0.0100 0.0008
## 140 0.7704 nan 0.0100 0.0003
## 160 0.7416 nan 0.0100 0.0004
## 180 0.7166 nan 0.0100 0.0004
## 200 0.6943 nan 0.0100 0.0001
## 220 0.6753 nan 0.0100 0.0000
## 240 0.6604 nan 0.0100 0.0001
## 260 0.6455 nan 0.0100 0.0000
## 280 0.6327 nan 0.0100 -0.0000
## 300 0.6201 nan 0.0100 0.0001
## 320 0.6083 nan 0.0100 -0.0001
## 340 0.5980 nan 0.0100 -0.0000
## 360 0.5884 nan 0.0100 -0.0000
## 380 0.5786 nan 0.0100 0.0000
## 400 0.5698 nan 0.0100 -0.0001
## 420 0.5610 nan 0.0100 -0.0001
## 440 0.5526 nan 0.0100 -0.0000
## 460 0.5442 nan 0.0100 -0.0000
## 480 0.5367 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3023 nan 0.0100 0.0041
## 3 1.2928 nan 0.0100 0.0041
## 4 1.2843 nan 0.0100 0.0038
## 5 1.2752 nan 0.0100 0.0043
## 6 1.2663 nan 0.0100 0.0043
## 7 1.2577 nan 0.0100 0.0038
## 8 1.2494 nan 0.0100 0.0039
## 9 1.2414 nan 0.0100 0.0035
## 10 1.2327 nan 0.0100 0.0038
## 20 1.1561 nan 0.0100 0.0033
## 40 1.0388 nan 0.0100 0.0023
## 60 0.9495 nan 0.0100 0.0018
## 80 0.8786 nan 0.0100 0.0013
## 100 0.8212 nan 0.0100 0.0010
## 120 0.7753 nan 0.0100 0.0008
## 140 0.7382 nan 0.0100 0.0004
## 160 0.7054 nan 0.0100 0.0003
## 180 0.6784 nan 0.0100 0.0003
## 200 0.6542 nan 0.0100 0.0002
## 220 0.6326 nan 0.0100 0.0001
## 240 0.6139 nan 0.0100 0.0001
## 260 0.5973 nan 0.0100 0.0001
## 280 0.5814 nan 0.0100 0.0002
## 300 0.5682 nan 0.0100 -0.0002
## 320 0.5545 nan 0.0100 0.0001
## 340 0.5429 nan 0.0100 -0.0001
## 360 0.5317 nan 0.0100 0.0001
## 380 0.5215 nan 0.0100 -0.0000
## 400 0.5116 nan 0.0100 -0.0001
## 420 0.5017 nan 0.0100 0.0000
## 440 0.4933 nan 0.0100 -0.0000
## 460 0.4837 nan 0.0100 -0.0001
## 480 0.4754 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2926 nan 0.0100 0.0042
## 4 1.2834 nan 0.0100 0.0043
## 5 1.2747 nan 0.0100 0.0040
## 6 1.2660 nan 0.0100 0.0037
## 7 1.2577 nan 0.0100 0.0036
## 8 1.2491 nan 0.0100 0.0038
## 9 1.2403 nan 0.0100 0.0040
## 10 1.2318 nan 0.0100 0.0037
## 20 1.1575 nan 0.0100 0.0028
## 40 1.0394 nan 0.0100 0.0023
## 60 0.9496 nan 0.0100 0.0014
## 80 0.8791 nan 0.0100 0.0014
## 100 0.8252 nan 0.0100 0.0010
## 120 0.7805 nan 0.0100 0.0005
## 140 0.7419 nan 0.0100 0.0006
## 160 0.7104 nan 0.0100 0.0006
## 180 0.6837 nan 0.0100 0.0003
## 200 0.6623 nan 0.0100 0.0002
## 220 0.6418 nan 0.0100 0.0002
## 240 0.6237 nan 0.0100 0.0000
## 260 0.6072 nan 0.0100 0.0001
## 280 0.5929 nan 0.0100 0.0001
## 300 0.5784 nan 0.0100 -0.0000
## 320 0.5648 nan 0.0100 -0.0001
## 340 0.5532 nan 0.0100 -0.0000
## 360 0.5421 nan 0.0100 -0.0001
## 380 0.5311 nan 0.0100 -0.0000
## 400 0.5215 nan 0.0100 0.0000
## 420 0.5120 nan 0.0100 -0.0000
## 440 0.5028 nan 0.0100 -0.0002
## 460 0.4941 nan 0.0100 -0.0001
## 480 0.4843 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0049
## 2 1.3023 nan 0.0100 0.0046
## 3 1.2936 nan 0.0100 0.0043
## 4 1.2852 nan 0.0100 0.0038
## 5 1.2762 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0036
## 7 1.2592 nan 0.0100 0.0036
## 8 1.2505 nan 0.0100 0.0041
## 9 1.2429 nan 0.0100 0.0038
## 10 1.2352 nan 0.0100 0.0038
## 20 1.1608 nan 0.0100 0.0031
## 40 1.0416 nan 0.0100 0.0023
## 60 0.9525 nan 0.0100 0.0017
## 80 0.8822 nan 0.0100 0.0013
## 100 0.8283 nan 0.0100 0.0008
## 120 0.7833 nan 0.0100 0.0006
## 140 0.7460 nan 0.0100 0.0006
## 160 0.7141 nan 0.0100 0.0003
## 180 0.6880 nan 0.0100 0.0003
## 200 0.6647 nan 0.0100 0.0003
## 220 0.6456 nan 0.0100 0.0002
## 240 0.6280 nan 0.0100 0.0003
## 260 0.6118 nan 0.0100 -0.0001
## 280 0.5972 nan 0.0100 -0.0000
## 300 0.5840 nan 0.0100 0.0001
## 320 0.5720 nan 0.0100 0.0001
## 340 0.5605 nan 0.0100 0.0001
## 360 0.5497 nan 0.0100 -0.0001
## 380 0.5390 nan 0.0100 -0.0001
## 400 0.5287 nan 0.0100 -0.0001
## 420 0.5189 nan 0.0100 0.0000
## 440 0.5101 nan 0.0100 0.0000
## 460 0.5011 nan 0.0100 0.0001
## 480 0.4929 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0048
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2915 nan 0.0100 0.0047
## 4 1.2813 nan 0.0100 0.0046
## 5 1.2729 nan 0.0100 0.0037
## 6 1.2636 nan 0.0100 0.0042
## 7 1.2543 nan 0.0100 0.0041
## 8 1.2456 nan 0.0100 0.0035
## 9 1.2373 nan 0.0100 0.0041
## 10 1.2286 nan 0.0100 0.0036
## 20 1.1510 nan 0.0100 0.0036
## 40 1.0260 nan 0.0100 0.0025
## 60 0.9328 nan 0.0100 0.0018
## 80 0.8596 nan 0.0100 0.0011
## 100 0.8009 nan 0.0100 0.0012
## 120 0.7524 nan 0.0100 0.0009
## 140 0.7122 nan 0.0100 0.0006
## 160 0.6782 nan 0.0100 0.0004
## 180 0.6505 nan 0.0100 0.0003
## 200 0.6253 nan 0.0100 0.0003
## 220 0.6027 nan 0.0100 0.0001
## 240 0.5823 nan 0.0100 0.0001
## 260 0.5650 nan 0.0100 0.0001
## 280 0.5485 nan 0.0100 0.0001
## 300 0.5331 nan 0.0100 0.0000
## 320 0.5193 nan 0.0100 -0.0000
## 340 0.5078 nan 0.0100 -0.0001
## 360 0.4945 nan 0.0100 0.0000
## 380 0.4830 nan 0.0100 0.0001
## 400 0.4710 nan 0.0100 0.0000
## 420 0.4609 nan 0.0100 0.0001
## 440 0.4509 nan 0.0100 0.0000
## 460 0.4417 nan 0.0100 -0.0002
## 480 0.4323 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0048
## 2 1.3004 nan 0.0100 0.0048
## 3 1.2908 nan 0.0100 0.0044
## 4 1.2817 nan 0.0100 0.0038
## 5 1.2719 nan 0.0100 0.0044
## 6 1.2626 nan 0.0100 0.0040
## 7 1.2537 nan 0.0100 0.0038
## 8 1.2442 nan 0.0100 0.0045
## 9 1.2359 nan 0.0100 0.0034
## 10 1.2273 nan 0.0100 0.0039
## 20 1.1502 nan 0.0100 0.0033
## 40 1.0278 nan 0.0100 0.0023
## 60 0.9350 nan 0.0100 0.0016
## 80 0.8607 nan 0.0100 0.0011
## 100 0.8036 nan 0.0100 0.0007
## 120 0.7573 nan 0.0100 0.0005
## 140 0.7182 nan 0.0100 0.0004
## 160 0.6840 nan 0.0100 0.0005
## 180 0.6555 nan 0.0100 0.0003
## 200 0.6328 nan 0.0100 0.0001
## 220 0.6110 nan 0.0100 0.0002
## 240 0.5920 nan 0.0100 0.0003
## 260 0.5750 nan 0.0100 -0.0000
## 280 0.5587 nan 0.0100 0.0001
## 300 0.5443 nan 0.0100 0.0001
## 320 0.5313 nan 0.0100 -0.0000
## 340 0.5184 nan 0.0100 -0.0000
## 360 0.5062 nan 0.0100 -0.0001
## 380 0.4946 nan 0.0100 -0.0001
## 400 0.4833 nan 0.0100 -0.0001
## 420 0.4737 nan 0.0100 -0.0001
## 440 0.4632 nan 0.0100 -0.0001
## 460 0.4531 nan 0.0100 0.0000
## 480 0.4434 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0046
## 2 1.3010 nan 0.0100 0.0050
## 3 1.2909 nan 0.0100 0.0049
## 4 1.2817 nan 0.0100 0.0044
## 5 1.2724 nan 0.0100 0.0045
## 6 1.2638 nan 0.0100 0.0039
## 7 1.2552 nan 0.0100 0.0040
## 8 1.2464 nan 0.0100 0.0038
## 9 1.2373 nan 0.0100 0.0039
## 10 1.2293 nan 0.0100 0.0035
## 20 1.1525 nan 0.0100 0.0029
## 40 1.0303 nan 0.0100 0.0026
## 60 0.9389 nan 0.0100 0.0018
## 80 0.8655 nan 0.0100 0.0014
## 100 0.8087 nan 0.0100 0.0008
## 120 0.7616 nan 0.0100 0.0005
## 140 0.7246 nan 0.0100 0.0005
## 160 0.6919 nan 0.0100 0.0004
## 180 0.6636 nan 0.0100 0.0002
## 200 0.6400 nan 0.0100 0.0000
## 220 0.6190 nan 0.0100 0.0000
## 240 0.6005 nan 0.0100 0.0000
## 260 0.5842 nan 0.0100 0.0002
## 280 0.5689 nan 0.0100 -0.0000
## 300 0.5543 nan 0.0100 0.0001
## 320 0.5406 nan 0.0100 -0.0000
## 340 0.5278 nan 0.0100 0.0000
## 360 0.5156 nan 0.0100 -0.0000
## 380 0.5044 nan 0.0100 0.0001
## 400 0.4935 nan 0.0100 -0.0000
## 420 0.4833 nan 0.0100 0.0001
## 440 0.4739 nan 0.0100 -0.0001
## 460 0.4645 nan 0.0100 -0.0001
## 480 0.4555 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2313 nan 0.1000 0.0401
## 2 1.1619 nan 0.1000 0.0303
## 3 1.0991 nan 0.1000 0.0261
## 4 1.0463 nan 0.1000 0.0204
## 5 0.9993 nan 0.1000 0.0211
## 6 0.9610 nan 0.1000 0.0163
## 7 0.9249 nan 0.1000 0.0145
## 8 0.8918 nan 0.1000 0.0129
## 9 0.8676 nan 0.1000 0.0092
## 10 0.8430 nan 0.1000 0.0098
## 20 0.6930 nan 0.1000 0.0014
## 40 0.5634 nan 0.1000 0.0010
## 60 0.4858 nan 0.1000 -0.0007
## 80 0.4347 nan 0.1000 -0.0014
## 100 0.3855 nan 0.1000 -0.0001
## 120 0.3451 nan 0.1000 -0.0006
## 140 0.3055 nan 0.1000 -0.0008
## 160 0.2741 nan 0.1000 -0.0005
## 180 0.2461 nan 0.1000 -0.0008
## 200 0.2197 nan 0.1000 -0.0006
## 220 0.2019 nan 0.1000 -0.0005
## 240 0.1833 nan 0.1000 -0.0002
## 260 0.1677 nan 0.1000 0.0000
## 280 0.1530 nan 0.1000 -0.0003
## 300 0.1392 nan 0.1000 -0.0003
## 320 0.1257 nan 0.1000 -0.0003
## 340 0.1143 nan 0.1000 -0.0004
## 360 0.1047 nan 0.1000 -0.0002
## 380 0.0967 nan 0.1000 -0.0004
## 400 0.0882 nan 0.1000 -0.0003
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## 460 0.0695 nan 0.1000 -0.0002
## 480 0.0648 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0386
## 2 1.1649 nan 0.1000 0.0358
## 3 1.1048 nan 0.1000 0.0283
## 4 1.0486 nan 0.1000 0.0241
## 5 1.0096 nan 0.1000 0.0159
## 6 0.9680 nan 0.1000 0.0189
## 7 0.9273 nan 0.1000 0.0176
## 8 0.8950 nan 0.1000 0.0132
## 9 0.8638 nan 0.1000 0.0124
## 10 0.8386 nan 0.1000 0.0096
## 20 0.6892 nan 0.1000 0.0018
## 40 0.5648 nan 0.1000 0.0002
## 60 0.4874 nan 0.1000 -0.0011
## 80 0.4313 nan 0.1000 0.0003
## 100 0.3890 nan 0.1000 -0.0009
## 120 0.3490 nan 0.1000 -0.0004
## 140 0.3119 nan 0.1000 -0.0010
## 160 0.2829 nan 0.1000 -0.0002
## 180 0.2567 nan 0.1000 -0.0006
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## 280 0.1562 nan 0.1000 -0.0003
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## 320 0.1313 nan 0.1000 -0.0005
## 340 0.1197 nan 0.1000 -0.0004
## 360 0.1094 nan 0.1000 -0.0004
## 380 0.1006 nan 0.1000 -0.0006
## 400 0.0932 nan 0.1000 -0.0003
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## 480 0.0672 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2350 nan 0.1000 0.0416
## 2 1.1605 nan 0.1000 0.0337
## 3 1.1020 nan 0.1000 0.0249
## 4 1.0483 nan 0.1000 0.0224
## 5 1.0019 nan 0.1000 0.0205
## 6 0.9647 nan 0.1000 0.0137
## 7 0.9349 nan 0.1000 0.0114
## 8 0.9046 nan 0.1000 0.0129
## 9 0.8776 nan 0.1000 0.0115
## 10 0.8534 nan 0.1000 0.0081
## 20 0.7043 nan 0.1000 -0.0003
## 40 0.5770 nan 0.1000 -0.0001
## 60 0.5032 nan 0.1000 -0.0004
## 80 0.4435 nan 0.1000 -0.0004
## 100 0.3954 nan 0.1000 -0.0006
## 120 0.3573 nan 0.1000 -0.0019
## 140 0.3235 nan 0.1000 -0.0014
## 160 0.2912 nan 0.1000 -0.0004
## 180 0.2658 nan 0.1000 -0.0006
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## 220 0.2251 nan 0.1000 -0.0007
## 240 0.2013 nan 0.1000 -0.0010
## 260 0.1834 nan 0.1000 -0.0001
## 280 0.1680 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2300 nan 0.1000 0.0410
## 2 1.1520 nan 0.1000 0.0382
## 3 1.0896 nan 0.1000 0.0251
## 4 1.0334 nan 0.1000 0.0255
## 5 0.9906 nan 0.1000 0.0166
## 6 0.9451 nan 0.1000 0.0201
## 7 0.9070 nan 0.1000 0.0138
## 8 0.8789 nan 0.1000 0.0122
## 9 0.8444 nan 0.1000 0.0130
## 10 0.8204 nan 0.1000 0.0064
## 20 0.6635 nan 0.1000 0.0041
## 40 0.5267 nan 0.1000 0.0005
## 60 0.4430 nan 0.1000 -0.0018
## 80 0.3726 nan 0.1000 -0.0002
## 100 0.3246 nan 0.1000 0.0003
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## 140 0.2384 nan 0.1000 0.0002
## 160 0.2064 nan 0.1000 -0.0001
## 180 0.1840 nan 0.1000 -0.0006
## 200 0.1648 nan 0.1000 -0.0002
## 220 0.1446 nan 0.1000 -0.0002
## 240 0.1270 nan 0.1000 -0.0000
## 260 0.1122 nan 0.1000 -0.0003
## 280 0.1004 nan 0.1000 -0.0002
## 300 0.0906 nan 0.1000 -0.0001
## 320 0.0813 nan 0.1000 -0.0002
## 340 0.0734 nan 0.1000 -0.0001
## 360 0.0662 nan 0.1000 -0.0002
## 380 0.0593 nan 0.1000 -0.0002
## 400 0.0534 nan 0.1000 -0.0001
## 420 0.0487 nan 0.1000 -0.0001
## 440 0.0448 nan 0.1000 -0.0001
## 460 0.0404 nan 0.1000 -0.0001
## 480 0.0374 nan 0.1000 -0.0001
## 500 0.0339 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0406
## 2 1.1568 nan 0.1000 0.0345
## 3 1.0983 nan 0.1000 0.0278
## 4 1.0415 nan 0.1000 0.0277
## 5 0.9966 nan 0.1000 0.0193
## 6 0.9524 nan 0.1000 0.0205
## 7 0.9113 nan 0.1000 0.0160
## 8 0.8782 nan 0.1000 0.0130
## 9 0.8468 nan 0.1000 0.0125
## 10 0.8200 nan 0.1000 0.0090
## 20 0.6569 nan 0.1000 0.0033
## 40 0.5171 nan 0.1000 -0.0008
## 60 0.4437 nan 0.1000 -0.0008
## 80 0.3709 nan 0.1000 0.0005
## 100 0.3186 nan 0.1000 -0.0011
## 120 0.2819 nan 0.1000 -0.0005
## 140 0.2460 nan 0.1000 -0.0011
## 160 0.2149 nan 0.1000 -0.0005
## 180 0.1917 nan 0.1000 -0.0009
## 200 0.1700 nan 0.1000 -0.0005
## 220 0.1504 nan 0.1000 -0.0006
## 240 0.1331 nan 0.1000 -0.0004
## 260 0.1196 nan 0.1000 -0.0005
## 280 0.1061 nan 0.1000 -0.0004
## 300 0.0950 nan 0.1000 -0.0003
## 320 0.0851 nan 0.1000 -0.0001
## 340 0.0775 nan 0.1000 -0.0002
## 360 0.0695 nan 0.1000 -0.0004
## 380 0.0625 nan 0.1000 -0.0002
## 400 0.0568 nan 0.1000 -0.0002
## 420 0.0512 nan 0.1000 -0.0002
## 440 0.0463 nan 0.1000 -0.0003
## 460 0.0418 nan 0.1000 -0.0001
## 480 0.0377 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0414
## 2 1.1578 nan 0.1000 0.0373
## 3 1.0914 nan 0.1000 0.0292
## 4 1.0424 nan 0.1000 0.0222
## 5 0.9980 nan 0.1000 0.0191
## 6 0.9545 nan 0.1000 0.0185
## 7 0.9174 nan 0.1000 0.0156
## 8 0.8821 nan 0.1000 0.0147
## 9 0.8490 nan 0.1000 0.0131
## 10 0.8256 nan 0.1000 0.0095
## 20 0.6669 nan 0.1000 0.0015
## 40 0.5275 nan 0.1000 0.0001
## 60 0.4547 nan 0.1000 -0.0012
## 80 0.3877 nan 0.1000 -0.0002
## 100 0.3378 nan 0.1000 -0.0006
## 120 0.2921 nan 0.1000 -0.0002
## 140 0.2547 nan 0.1000 0.0001
## 160 0.2260 nan 0.1000 -0.0015
## 180 0.2027 nan 0.1000 -0.0001
## 200 0.1816 nan 0.1000 -0.0010
## 220 0.1607 nan 0.1000 -0.0010
## 240 0.1440 nan 0.1000 -0.0006
## 260 0.1286 nan 0.1000 -0.0001
## 280 0.1140 nan 0.1000 -0.0003
## 300 0.1025 nan 0.1000 -0.0005
## 320 0.0938 nan 0.1000 -0.0006
## 340 0.0839 nan 0.1000 -0.0004
## 360 0.0767 nan 0.1000 -0.0005
## 380 0.0696 nan 0.1000 -0.0005
## 400 0.0635 nan 0.1000 -0.0002
## 420 0.0571 nan 0.1000 -0.0003
## 440 0.0519 nan 0.1000 -0.0001
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## 480 0.0428 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2223 nan 0.1000 0.0478
## 2 1.1443 nan 0.1000 0.0373
## 3 1.0760 nan 0.1000 0.0256
## 4 1.0172 nan 0.1000 0.0268
## 5 0.9744 nan 0.1000 0.0176
## 6 0.9348 nan 0.1000 0.0153
## 7 0.8922 nan 0.1000 0.0187
## 8 0.8599 nan 0.1000 0.0108
## 9 0.8292 nan 0.1000 0.0117
## 10 0.8009 nan 0.1000 0.0109
## 20 0.6324 nan 0.1000 0.0014
## 40 0.4825 nan 0.1000 -0.0011
## 60 0.3894 nan 0.1000 -0.0018
## 80 0.3234 nan 0.1000 0.0008
## 100 0.2707 nan 0.1000 -0.0004
## 120 0.2280 nan 0.1000 -0.0004
## 140 0.1949 nan 0.1000 -0.0005
## 160 0.1691 nan 0.1000 -0.0001
## 180 0.1436 nan 0.1000 -0.0003
## 200 0.1232 nan 0.1000 -0.0003
## 220 0.1076 nan 0.1000 -0.0003
## 240 0.0957 nan 0.1000 -0.0003
## 260 0.0825 nan 0.1000 -0.0002
## 280 0.0724 nan 0.1000 -0.0002
## 300 0.0632 nan 0.1000 -0.0002
## 320 0.0565 nan 0.1000 -0.0001
## 340 0.0492 nan 0.1000 -0.0001
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## 380 0.0387 nan 0.1000 -0.0001
## 400 0.0341 nan 0.1000 -0.0002
## 420 0.0302 nan 0.1000 -0.0000
## 440 0.0267 nan 0.1000 -0.0001
## 460 0.0236 nan 0.1000 -0.0001
## 480 0.0207 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2207 nan 0.1000 0.0493
## 2 1.1468 nan 0.1000 0.0349
## 3 1.0759 nan 0.1000 0.0342
## 4 1.0255 nan 0.1000 0.0223
## 5 0.9775 nan 0.1000 0.0167
## 6 0.9384 nan 0.1000 0.0155
## 7 0.8989 nan 0.1000 0.0148
## 8 0.8645 nan 0.1000 0.0126
## 9 0.8369 nan 0.1000 0.0109
## 10 0.8064 nan 0.1000 0.0114
## 20 0.6389 nan 0.1000 0.0005
## 40 0.4858 nan 0.1000 -0.0003
## 60 0.4004 nan 0.1000 -0.0014
## 80 0.3318 nan 0.1000 -0.0022
## 100 0.2814 nan 0.1000 -0.0021
## 120 0.2390 nan 0.1000 -0.0003
## 140 0.2061 nan 0.1000 -0.0011
## 160 0.1737 nan 0.1000 -0.0005
## 180 0.1484 nan 0.1000 -0.0006
## 200 0.1276 nan 0.1000 -0.0005
## 220 0.1127 nan 0.1000 -0.0008
## 240 0.0996 nan 0.1000 -0.0002
## 260 0.0879 nan 0.1000 -0.0002
## 280 0.0771 nan 0.1000 -0.0003
## 300 0.0675 nan 0.1000 -0.0001
## 320 0.0597 nan 0.1000 -0.0001
## 340 0.0529 nan 0.1000 -0.0001
## 360 0.0474 nan 0.1000 -0.0002
## 380 0.0422 nan 0.1000 -0.0003
## 400 0.0370 nan 0.1000 -0.0002
## 420 0.0324 nan 0.1000 -0.0001
## 440 0.0287 nan 0.1000 -0.0001
## 460 0.0251 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0001
## 500 0.0195 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2288 nan 0.1000 0.0451
## 2 1.1429 nan 0.1000 0.0389
## 3 1.0721 nan 0.1000 0.0314
## 4 1.0175 nan 0.1000 0.0254
## 5 0.9648 nan 0.1000 0.0212
## 6 0.9212 nan 0.1000 0.0161
## 7 0.8886 nan 0.1000 0.0127
## 8 0.8552 nan 0.1000 0.0123
## 9 0.8265 nan 0.1000 0.0108
## 10 0.8015 nan 0.1000 0.0082
## 20 0.6378 nan 0.1000 0.0035
## 40 0.4956 nan 0.1000 -0.0005
## 60 0.4046 nan 0.1000 -0.0017
## 80 0.3421 nan 0.1000 -0.0009
## 100 0.2931 nan 0.1000 -0.0008
## 120 0.2520 nan 0.1000 -0.0020
## 140 0.2161 nan 0.1000 -0.0009
## 160 0.1872 nan 0.1000 -0.0009
## 180 0.1633 nan 0.1000 -0.0003
## 200 0.1425 nan 0.1000 -0.0009
## 220 0.1228 nan 0.1000 -0.0003
## 240 0.1090 nan 0.1000 -0.0006
## 260 0.0966 nan 0.1000 -0.0005
## 280 0.0853 nan 0.1000 -0.0003
## 300 0.0751 nan 0.1000 -0.0003
## 320 0.0667 nan 0.1000 -0.0003
## 340 0.0585 nan 0.1000 -0.0003
## 360 0.0523 nan 0.1000 -0.0001
## 380 0.0463 nan 0.1000 -0.0004
## 400 0.0415 nan 0.1000 -0.0002
## 420 0.0372 nan 0.1000 -0.0001
## 440 0.0334 nan 0.1000 -0.0001
## 460 0.0294 nan 0.1000 -0.0000
## 480 0.0265 nan 0.1000 -0.0002
## 500 0.0234 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2873 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0003
## 80 1.2564 nan 0.0010 0.0003
## 100 1.2424 nan 0.0010 0.0003
## 120 1.2284 nan 0.0010 0.0003
## 140 1.2148 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1889 nan 0.0010 0.0002
## 200 1.1765 nan 0.0010 0.0003
## 220 1.1643 nan 0.0010 0.0003
## 240 1.1524 nan 0.0010 0.0003
## 260 1.1409 nan 0.0010 0.0002
## 280 1.1296 nan 0.0010 0.0003
## 300 1.1191 nan 0.0010 0.0003
## 320 1.1087 nan 0.0010 0.0002
## 340 1.0985 nan 0.0010 0.0002
## 360 1.0885 nan 0.0010 0.0002
## 380 1.0791 nan 0.0010 0.0002
## 400 1.0698 nan 0.0010 0.0002
## 420 1.0606 nan 0.0010 0.0002
## 440 1.0517 nan 0.0010 0.0002
## 460 1.0430 nan 0.0010 0.0002
## 480 1.0346 nan 0.0010 0.0002
## 500 1.0263 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3191 nan 0.0010 0.0003
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0003
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0004
## 60 1.2724 nan 0.0010 0.0003
## 80 1.2570 nan 0.0010 0.0003
## 100 1.2429 nan 0.0010 0.0003
## 120 1.2291 nan 0.0010 0.0003
## 140 1.2153 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1891 nan 0.0010 0.0003
## 200 1.1766 nan 0.0010 0.0003
## 220 1.1644 nan 0.0010 0.0003
## 240 1.1527 nan 0.0010 0.0003
## 260 1.1413 nan 0.0010 0.0003
## 280 1.1300 nan 0.0010 0.0002
## 300 1.1193 nan 0.0010 0.0002
## 320 1.1093 nan 0.0010 0.0003
## 340 1.0992 nan 0.0010 0.0002
## 360 1.0893 nan 0.0010 0.0002
## 380 1.0796 nan 0.0010 0.0002
## 400 1.0703 nan 0.0010 0.0002
## 420 1.0612 nan 0.0010 0.0002
## 440 1.0522 nan 0.0010 0.0002
## 460 1.0434 nan 0.0010 0.0002
## 480 1.0349 nan 0.0010 0.0001
## 500 1.0270 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0003
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3135 nan 0.0010 0.0003
## 10 1.3127 nan 0.0010 0.0003
## 20 1.3048 nan 0.0010 0.0003
## 40 1.2884 nan 0.0010 0.0004
## 60 1.2730 nan 0.0010 0.0003
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2297 nan 0.0010 0.0003
## 140 1.2161 nan 0.0010 0.0003
## 160 1.2032 nan 0.0010 0.0003
## 180 1.1905 nan 0.0010 0.0003
## 200 1.1783 nan 0.0010 0.0002
## 220 1.1663 nan 0.0010 0.0003
## 240 1.1546 nan 0.0010 0.0003
## 260 1.1432 nan 0.0010 0.0003
## 280 1.1323 nan 0.0010 0.0002
## 300 1.1216 nan 0.0010 0.0002
## 320 1.1111 nan 0.0010 0.0002
## 340 1.1011 nan 0.0010 0.0002
## 360 1.0912 nan 0.0010 0.0003
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0722 nan 0.0010 0.0002
## 420 1.0631 nan 0.0010 0.0001
## 440 1.0543 nan 0.0010 0.0002
## 460 1.0457 nan 0.0010 0.0002
## 480 1.0374 nan 0.0010 0.0002
## 500 1.0292 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2855 nan 0.0010 0.0004
## 60 1.2688 nan 0.0010 0.0004
## 80 1.2527 nan 0.0010 0.0004
## 100 1.2371 nan 0.0010 0.0003
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2074 nan 0.0010 0.0003
## 160 1.1936 nan 0.0010 0.0003
## 180 1.1796 nan 0.0010 0.0003
## 200 1.1663 nan 0.0010 0.0003
## 220 1.1533 nan 0.0010 0.0003
## 240 1.1408 nan 0.0010 0.0002
## 260 1.1287 nan 0.0010 0.0002
## 280 1.1169 nan 0.0010 0.0002
## 300 1.1058 nan 0.0010 0.0002
## 320 1.0946 nan 0.0010 0.0002
## 340 1.0835 nan 0.0010 0.0002
## 360 1.0734 nan 0.0010 0.0002
## 380 1.0630 nan 0.0010 0.0002
## 400 1.0531 nan 0.0010 0.0002
## 420 1.0434 nan 0.0010 0.0002
## 440 1.0342 nan 0.0010 0.0002
## 460 1.0249 nan 0.0010 0.0002
## 480 1.0156 nan 0.0010 0.0002
## 500 1.0070 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2689 nan 0.0010 0.0004
## 80 1.2530 nan 0.0010 0.0004
## 100 1.2377 nan 0.0010 0.0004
## 120 1.2228 nan 0.0010 0.0003
## 140 1.2085 nan 0.0010 0.0004
## 160 1.1946 nan 0.0010 0.0002
## 180 1.1811 nan 0.0010 0.0003
## 200 1.1680 nan 0.0010 0.0002
## 220 1.1553 nan 0.0010 0.0003
## 240 1.1430 nan 0.0010 0.0002
## 260 1.1310 nan 0.0010 0.0003
## 280 1.1194 nan 0.0010 0.0002
## 300 1.1080 nan 0.0010 0.0003
## 320 1.0969 nan 0.0010 0.0002
## 340 1.0863 nan 0.0010 0.0002
## 360 1.0757 nan 0.0010 0.0002
## 380 1.0653 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
## 440 1.0366 nan 0.0010 0.0002
## 460 1.0276 nan 0.0010 0.0002
## 480 1.0185 nan 0.0010 0.0002
## 500 1.0098 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0003
## 20 1.3033 nan 0.0010 0.0003
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2546 nan 0.0010 0.0003
## 100 1.2398 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2111 nan 0.0010 0.0003
## 160 1.1972 nan 0.0010 0.0003
## 180 1.1839 nan 0.0010 0.0003
## 200 1.1711 nan 0.0010 0.0003
## 220 1.1582 nan 0.0010 0.0003
## 240 1.1458 nan 0.0010 0.0002
## 260 1.1338 nan 0.0010 0.0002
## 280 1.1220 nan 0.0010 0.0003
## 300 1.1107 nan 0.0010 0.0002
## 320 1.0998 nan 0.0010 0.0002
## 340 1.0891 nan 0.0010 0.0002
## 360 1.0788 nan 0.0010 0.0002
## 380 1.0687 nan 0.0010 0.0002
## 400 1.0588 nan 0.0010 0.0002
## 420 1.0493 nan 0.0010 0.0002
## 440 1.0399 nan 0.0010 0.0002
## 460 1.0308 nan 0.0010 0.0002
## 480 1.0221 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2497 nan 0.0010 0.0004
## 100 1.2337 nan 0.0010 0.0003
## 120 1.2178 nan 0.0010 0.0003
## 140 1.2025 nan 0.0010 0.0003
## 160 1.1874 nan 0.0010 0.0003
## 180 1.1729 nan 0.0010 0.0003
## 200 1.1593 nan 0.0010 0.0003
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## 240 1.1333 nan 0.0010 0.0003
## 260 1.1206 nan 0.0010 0.0002
## 280 1.1083 nan 0.0010 0.0002
## 300 1.0964 nan 0.0010 0.0002
## 320 1.0849 nan 0.0010 0.0002
## 340 1.0735 nan 0.0010 0.0003
## 360 1.0624 nan 0.0010 0.0002
## 380 1.0516 nan 0.0010 0.0002
## 400 1.0410 nan 0.0010 0.0003
## 420 1.0310 nan 0.0010 0.0002
## 440 1.0210 nan 0.0010 0.0002
## 460 1.0115 nan 0.0010 0.0002
## 480 1.0021 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0003
## 60 1.2671 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2039 nan 0.0010 0.0003
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## 180 1.1750 nan 0.0010 0.0003
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## 220 1.1481 nan 0.0010 0.0002
## 240 1.1351 nan 0.0010 0.0003
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## 320 1.0868 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0648 nan 0.0010 0.0002
## 380 1.0542 nan 0.0010 0.0003
## 400 1.0437 nan 0.0010 0.0002
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## 480 1.0051 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
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## 10 1.3115 nan 0.0010 0.0003
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0004
## 120 1.2203 nan 0.0010 0.0004
## 140 1.2054 nan 0.0010 0.0003
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## 320 1.0906 nan 0.0010 0.0002
## 340 1.0797 nan 0.0010 0.0003
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## 380 1.0583 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0038
## 2 1.3043 nan 0.0100 0.0034
## 3 1.2963 nan 0.0100 0.0037
## 4 1.2875 nan 0.0100 0.0041
## 5 1.2807 nan 0.0100 0.0031
## 6 1.2736 nan 0.0100 0.0028
## 7 1.2661 nan 0.0100 0.0034
## 8 1.2580 nan 0.0100 0.0034
## 9 1.2505 nan 0.0100 0.0031
## 10 1.2426 nan 0.0100 0.0037
## 20 1.1759 nan 0.0100 0.0027
## 40 1.0692 nan 0.0100 0.0017
## 60 0.9874 nan 0.0100 0.0016
## 80 0.9246 nan 0.0100 0.0010
## 100 0.8693 nan 0.0100 0.0011
## 120 0.8263 nan 0.0100 0.0007
## 140 0.7894 nan 0.0100 0.0007
## 160 0.7580 nan 0.0100 0.0003
## 180 0.7326 nan 0.0100 0.0004
## 200 0.7114 nan 0.0100 0.0001
## 220 0.6919 nan 0.0100 0.0002
## 240 0.6736 nan 0.0100 0.0001
## 260 0.6579 nan 0.0100 0.0001
## 280 0.6432 nan 0.0100 0.0001
## 300 0.6317 nan 0.0100 -0.0000
## 320 0.6200 nan 0.0100 0.0001
## 340 0.6083 nan 0.0100 -0.0001
## 360 0.5966 nan 0.0100 -0.0000
## 380 0.5871 nan 0.0100 -0.0000
## 400 0.5775 nan 0.0100 -0.0000
## 420 0.5674 nan 0.0100 -0.0001
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## 460 0.5503 nan 0.0100 -0.0000
## 480 0.5414 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3037 nan 0.0100 0.0033
## 3 1.2953 nan 0.0100 0.0040
## 4 1.2874 nan 0.0100 0.0036
## 5 1.2794 nan 0.0100 0.0039
## 6 1.2716 nan 0.0100 0.0037
## 7 1.2640 nan 0.0100 0.0036
## 8 1.2567 nan 0.0100 0.0033
## 9 1.2491 nan 0.0100 0.0039
## 10 1.2423 nan 0.0100 0.0032
## 20 1.1740 nan 0.0100 0.0023
## 40 1.0686 nan 0.0100 0.0022
## 60 0.9868 nan 0.0100 0.0014
## 80 0.9221 nan 0.0100 0.0010
## 100 0.8702 nan 0.0100 0.0009
## 120 0.8272 nan 0.0100 0.0005
## 140 0.7930 nan 0.0100 0.0004
## 160 0.7651 nan 0.0100 0.0004
## 180 0.7391 nan 0.0100 0.0003
## 200 0.7164 nan 0.0100 0.0001
## 220 0.6954 nan 0.0100 0.0002
## 240 0.6778 nan 0.0100 0.0003
## 260 0.6619 nan 0.0100 0.0004
## 280 0.6488 nan 0.0100 -0.0000
## 300 0.6360 nan 0.0100 -0.0002
## 320 0.6245 nan 0.0100 0.0001
## 340 0.6131 nan 0.0100 0.0000
## 360 0.6026 nan 0.0100 -0.0001
## 380 0.5918 nan 0.0100 -0.0000
## 400 0.5817 nan 0.0100 0.0000
## 420 0.5714 nan 0.0100 -0.0000
## 440 0.5619 nan 0.0100 0.0000
## 460 0.5524 nan 0.0100 0.0001
## 480 0.5439 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3132 nan 0.0100 0.0038
## 2 1.3053 nan 0.0100 0.0039
## 3 1.2965 nan 0.0100 0.0039
## 4 1.2890 nan 0.0100 0.0031
## 5 1.2813 nan 0.0100 0.0030
## 6 1.2733 nan 0.0100 0.0036
## 7 1.2654 nan 0.0100 0.0037
## 8 1.2582 nan 0.0100 0.0034
## 9 1.2506 nan 0.0100 0.0033
## 10 1.2428 nan 0.0100 0.0034
## 20 1.1791 nan 0.0100 0.0023
## 40 1.0725 nan 0.0100 0.0016
## 60 0.9909 nan 0.0100 0.0017
## 80 0.9253 nan 0.0100 0.0010
## 100 0.8734 nan 0.0100 0.0008
## 120 0.8305 nan 0.0100 0.0006
## 140 0.7957 nan 0.0100 0.0006
## 160 0.7661 nan 0.0100 0.0004
## 180 0.7418 nan 0.0100 0.0001
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## 220 0.7011 nan 0.0100 0.0001
## 240 0.6841 nan 0.0100 0.0002
## 260 0.6681 nan 0.0100 0.0002
## 280 0.6549 nan 0.0100 0.0001
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## 320 0.6295 nan 0.0100 0.0001
## 340 0.6187 nan 0.0100 0.0000
## 360 0.6070 nan 0.0100 0.0000
## 380 0.5969 nan 0.0100 -0.0000
## 400 0.5875 nan 0.0100 -0.0000
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## 460 0.5592 nan 0.0100 -0.0001
## 480 0.5509 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0045
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2929 nan 0.0100 0.0042
## 4 1.2841 nan 0.0100 0.0037
## 5 1.2762 nan 0.0100 0.0039
## 6 1.2680 nan 0.0100 0.0037
## 7 1.2605 nan 0.0100 0.0031
## 8 1.2519 nan 0.0100 0.0036
## 9 1.2443 nan 0.0100 0.0033
## 10 1.2370 nan 0.0100 0.0032
## 20 1.1662 nan 0.0100 0.0031
## 40 1.0536 nan 0.0100 0.0017
## 60 0.9664 nan 0.0100 0.0018
## 80 0.8991 nan 0.0100 0.0013
## 100 0.8440 nan 0.0100 0.0010
## 120 0.7983 nan 0.0100 0.0008
## 140 0.7608 nan 0.0100 0.0005
## 160 0.7283 nan 0.0100 0.0002
## 180 0.7020 nan 0.0100 0.0002
## 200 0.6778 nan 0.0100 0.0001
## 220 0.6560 nan 0.0100 0.0003
## 240 0.6354 nan 0.0100 0.0001
## 260 0.6178 nan 0.0100 0.0002
## 280 0.6020 nan 0.0100 0.0000
## 300 0.5867 nan 0.0100 0.0001
## 320 0.5737 nan 0.0100 0.0001
## 340 0.5613 nan 0.0100 0.0001
## 360 0.5488 nan 0.0100 0.0002
## 380 0.5371 nan 0.0100 -0.0001
## 400 0.5263 nan 0.0100 -0.0000
## 420 0.5158 nan 0.0100 -0.0001
## 440 0.5058 nan 0.0100 -0.0000
## 460 0.4952 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0042
## 2 1.3034 nan 0.0100 0.0044
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2858 nan 0.0100 0.0039
## 5 1.2773 nan 0.0100 0.0039
## 6 1.2687 nan 0.0100 0.0035
## 7 1.2601 nan 0.0100 0.0040
## 8 1.2519 nan 0.0100 0.0038
## 9 1.2444 nan 0.0100 0.0034
## 10 1.2362 nan 0.0100 0.0036
## 20 1.1650 nan 0.0100 0.0031
## 40 1.0547 nan 0.0100 0.0018
## 60 0.9698 nan 0.0100 0.0016
## 80 0.9000 nan 0.0100 0.0015
## 100 0.8445 nan 0.0100 0.0010
## 120 0.7998 nan 0.0100 0.0008
## 140 0.7636 nan 0.0100 0.0005
## 160 0.7325 nan 0.0100 0.0006
## 180 0.7072 nan 0.0100 0.0003
## 200 0.6821 nan 0.0100 0.0002
## 220 0.6614 nan 0.0100 0.0004
## 240 0.6425 nan 0.0100 0.0002
## 260 0.6257 nan 0.0100 0.0002
## 280 0.6109 nan 0.0100 -0.0000
## 300 0.5961 nan 0.0100 0.0001
## 320 0.5829 nan 0.0100 -0.0000
## 340 0.5701 nan 0.0100 -0.0003
## 360 0.5584 nan 0.0100 -0.0001
## 380 0.5468 nan 0.0100 -0.0000
## 400 0.5372 nan 0.0100 -0.0000
## 420 0.5265 nan 0.0100 0.0001
## 440 0.5164 nan 0.0100 0.0002
## 460 0.5074 nan 0.0100 -0.0001
## 480 0.4972 nan 0.0100 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0038
## 2 1.3030 nan 0.0100 0.0042
## 3 1.2951 nan 0.0100 0.0036
## 4 1.2869 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0040
## 6 1.2703 nan 0.0100 0.0034
## 7 1.2624 nan 0.0100 0.0034
## 8 1.2541 nan 0.0100 0.0035
## 9 1.2467 nan 0.0100 0.0029
## 10 1.2394 nan 0.0100 0.0031
## 20 1.1696 nan 0.0100 0.0027
## 40 1.0609 nan 0.0100 0.0017
## 60 0.9767 nan 0.0100 0.0013
## 80 0.9090 nan 0.0100 0.0012
## 100 0.8560 nan 0.0100 0.0009
## 120 0.8103 nan 0.0100 0.0007
## 140 0.7744 nan 0.0100 0.0007
## 160 0.7435 nan 0.0100 0.0003
## 180 0.7164 nan 0.0100 0.0004
## 200 0.6923 nan 0.0100 0.0004
## 220 0.6718 nan 0.0100 0.0002
## 240 0.6528 nan 0.0100 0.0002
## 260 0.6346 nan 0.0100 0.0000
## 280 0.6193 nan 0.0100 0.0001
## 300 0.6043 nan 0.0100 0.0001
## 320 0.5906 nan 0.0100 -0.0001
## 340 0.5778 nan 0.0100 -0.0000
## 360 0.5657 nan 0.0100 -0.0001
## 380 0.5542 nan 0.0100 -0.0000
## 400 0.5433 nan 0.0100 -0.0001
## 420 0.5332 nan 0.0100 -0.0000
## 440 0.5230 nan 0.0100 -0.0001
## 460 0.5137 nan 0.0100 0.0001
## 480 0.5036 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0045
## 2 1.3009 nan 0.0100 0.0042
## 3 1.2905 nan 0.0100 0.0043
## 4 1.2822 nan 0.0100 0.0037
## 5 1.2722 nan 0.0100 0.0039
## 6 1.2637 nan 0.0100 0.0042
## 7 1.2560 nan 0.0100 0.0033
## 8 1.2481 nan 0.0100 0.0036
## 9 1.2403 nan 0.0100 0.0034
## 10 1.2320 nan 0.0100 0.0034
## 20 1.1556 nan 0.0100 0.0031
## 40 1.0387 nan 0.0100 0.0024
## 60 0.9495 nan 0.0100 0.0015
## 80 0.8786 nan 0.0100 0.0013
## 100 0.8210 nan 0.0100 0.0012
## 120 0.7745 nan 0.0100 0.0007
## 140 0.7346 nan 0.0100 0.0006
## 160 0.7014 nan 0.0100 0.0004
## 180 0.6738 nan 0.0100 0.0004
## 200 0.6475 nan 0.0100 0.0002
## 220 0.6242 nan 0.0100 0.0001
## 240 0.6033 nan 0.0100 -0.0000
## 260 0.5851 nan 0.0100 0.0001
## 280 0.5677 nan 0.0100 0.0001
## 300 0.5518 nan 0.0100 0.0001
## 320 0.5369 nan 0.0100 0.0001
## 340 0.5228 nan 0.0100 0.0000
## 360 0.5093 nan 0.0100 0.0002
## 380 0.4953 nan 0.0100 0.0001
## 400 0.4841 nan 0.0100 0.0001
## 420 0.4730 nan 0.0100 0.0001
## 440 0.4624 nan 0.0100 -0.0002
## 460 0.4527 nan 0.0100 -0.0001
## 480 0.4426 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0043
## 2 1.3012 nan 0.0100 0.0044
## 3 1.2927 nan 0.0100 0.0038
## 4 1.2842 nan 0.0100 0.0043
## 5 1.2754 nan 0.0100 0.0042
## 6 1.2666 nan 0.0100 0.0039
## 7 1.2583 nan 0.0100 0.0034
## 8 1.2500 nan 0.0100 0.0039
## 9 1.2413 nan 0.0100 0.0038
## 10 1.2333 nan 0.0100 0.0036
## 20 1.1599 nan 0.0100 0.0027
## 40 1.0445 nan 0.0100 0.0023
## 60 0.9554 nan 0.0100 0.0016
## 80 0.8860 nan 0.0100 0.0014
## 100 0.8306 nan 0.0100 0.0008
## 120 0.7837 nan 0.0100 0.0006
## 140 0.7445 nan 0.0100 0.0008
## 160 0.7111 nan 0.0100 0.0004
## 180 0.6822 nan 0.0100 0.0004
## 200 0.6573 nan 0.0100 0.0003
## 220 0.6342 nan 0.0100 0.0000
## 240 0.6136 nan 0.0100 -0.0000
## 260 0.5950 nan 0.0100 0.0001
## 280 0.5793 nan 0.0100 -0.0000
## 300 0.5630 nan 0.0100 -0.0001
## 320 0.5483 nan 0.0100 -0.0001
## 340 0.5339 nan 0.0100 0.0001
## 360 0.5206 nan 0.0100 0.0001
## 380 0.5073 nan 0.0100 -0.0000
## 400 0.4956 nan 0.0100 0.0001
## 420 0.4836 nan 0.0100 0.0001
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## 460 0.4615 nan 0.0100 0.0000
## 480 0.4524 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3027 nan 0.0100 0.0036
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2847 nan 0.0100 0.0037
## 5 1.2760 nan 0.0100 0.0042
## 6 1.2673 nan 0.0100 0.0035
## 7 1.2592 nan 0.0100 0.0035
## 8 1.2509 nan 0.0100 0.0039
## 9 1.2430 nan 0.0100 0.0036
## 10 1.2346 nan 0.0100 0.0035
## 20 1.1624 nan 0.0100 0.0023
## 40 1.0485 nan 0.0100 0.0020
## 60 0.9606 nan 0.0100 0.0018
## 80 0.8892 nan 0.0100 0.0011
## 100 0.8334 nan 0.0100 0.0009
## 120 0.7898 nan 0.0100 0.0005
## 140 0.7508 nan 0.0100 0.0005
## 160 0.7183 nan 0.0100 0.0006
## 180 0.6911 nan 0.0100 0.0002
## 200 0.6658 nan 0.0100 0.0002
## 220 0.6433 nan 0.0100 0.0002
## 240 0.6222 nan 0.0100 0.0000
## 260 0.6032 nan 0.0100 -0.0001
## 280 0.5865 nan 0.0100 -0.0000
## 300 0.5701 nan 0.0100 0.0001
## 320 0.5549 nan 0.0100 0.0000
## 340 0.5417 nan 0.0100 -0.0002
## 360 0.5286 nan 0.0100 0.0001
## 380 0.5171 nan 0.0100 0.0000
## 400 0.5054 nan 0.0100 -0.0000
## 420 0.4941 nan 0.0100 -0.0002
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## 460 0.4732 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2447 nan 0.1000 0.0344
## 2 1.1709 nan 0.1000 0.0304
## 3 1.1140 nan 0.1000 0.0274
## 4 1.0649 nan 0.1000 0.0200
## 5 1.0277 nan 0.1000 0.0168
## 6 0.9906 nan 0.1000 0.0150
## 7 0.9513 nan 0.1000 0.0145
## 8 0.9248 nan 0.1000 0.0106
## 9 0.8956 nan 0.1000 0.0122
## 10 0.8747 nan 0.1000 0.0066
## 20 0.7203 nan 0.1000 0.0031
## 40 0.5863 nan 0.1000 -0.0004
## 60 0.5004 nan 0.1000 0.0009
## 80 0.4303 nan 0.1000 -0.0007
## 100 0.3850 nan 0.1000 -0.0003
## 120 0.3405 nan 0.1000 -0.0003
## 140 0.3035 nan 0.1000 -0.0006
## 160 0.2707 nan 0.1000 -0.0003
## 180 0.2432 nan 0.1000 -0.0005
## 200 0.2195 nan 0.1000 0.0001
## 220 0.1980 nan 0.1000 -0.0008
## 240 0.1773 nan 0.1000 -0.0007
## 260 0.1621 nan 0.1000 -0.0005
## 280 0.1473 nan 0.1000 -0.0005
## 300 0.1329 nan 0.1000 -0.0000
## 320 0.1208 nan 0.1000 0.0003
## 340 0.1102 nan 0.1000 -0.0001
## 360 0.1007 nan 0.1000 -0.0004
## 380 0.0927 nan 0.1000 -0.0001
## 400 0.0843 nan 0.1000 -0.0002
## 420 0.0777 nan 0.1000 -0.0003
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## 460 0.0647 nan 0.1000 -0.0002
## 480 0.0591 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2460 nan 0.1000 0.0332
## 2 1.1701 nan 0.1000 0.0333
## 3 1.1088 nan 0.1000 0.0292
## 4 1.0586 nan 0.1000 0.0215
## 5 1.0169 nan 0.1000 0.0173
## 6 0.9822 nan 0.1000 0.0156
## 7 0.9517 nan 0.1000 0.0129
## 8 0.9211 nan 0.1000 0.0117
## 9 0.8918 nan 0.1000 0.0135
## 10 0.8710 nan 0.1000 0.0070
## 20 0.7252 nan 0.1000 0.0004
## 40 0.5905 nan 0.1000 -0.0011
## 60 0.5149 nan 0.1000 -0.0009
## 80 0.4447 nan 0.1000 0.0000
## 100 0.3942 nan 0.1000 -0.0018
## 120 0.3556 nan 0.1000 -0.0011
## 140 0.3178 nan 0.1000 0.0005
## 160 0.2871 nan 0.1000 -0.0004
## 180 0.2540 nan 0.1000 -0.0007
## 200 0.2316 nan 0.1000 -0.0008
## 220 0.2095 nan 0.1000 -0.0005
## 240 0.1890 nan 0.1000 -0.0001
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## 280 0.1542 nan 0.1000 -0.0001
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## 320 0.1283 nan 0.1000 -0.0005
## 340 0.1170 nan 0.1000 -0.0003
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## 380 0.0988 nan 0.1000 -0.0003
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## 480 0.0658 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2390 nan 0.1000 0.0361
## 2 1.1672 nan 0.1000 0.0327
## 3 1.1118 nan 0.1000 0.0198
## 4 1.0591 nan 0.1000 0.0213
## 5 1.0184 nan 0.1000 0.0160
## 6 0.9817 nan 0.1000 0.0178
## 7 0.9459 nan 0.1000 0.0131
## 8 0.9151 nan 0.1000 0.0142
## 9 0.8890 nan 0.1000 0.0112
## 10 0.8648 nan 0.1000 0.0106
## 20 0.7173 nan 0.1000 0.0007
## 40 0.5817 nan 0.1000 -0.0001
## 60 0.4991 nan 0.1000 -0.0006
## 80 0.4376 nan 0.1000 -0.0006
## 100 0.3834 nan 0.1000 0.0002
## 120 0.3418 nan 0.1000 0.0002
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2820 nan 0.1000 -0.0009
## 180 0.2514 nan 0.1000 -0.0003
## 200 0.2267 nan 0.1000 -0.0002
## 220 0.2052 nan 0.1000 -0.0006
## 240 0.1848 nan 0.1000 -0.0000
## 260 0.1680 nan 0.1000 -0.0003
## 280 0.1523 nan 0.1000 -0.0008
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## 320 0.1253 nan 0.1000 -0.0004
## 340 0.1150 nan 0.1000 -0.0003
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## 380 0.0962 nan 0.1000 -0.0003
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## 460 0.0694 nan 0.1000 -0.0001
## 480 0.0636 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0408
## 2 1.1597 nan 0.1000 0.0315
## 3 1.0987 nan 0.1000 0.0278
## 4 1.0450 nan 0.1000 0.0214
## 5 1.0017 nan 0.1000 0.0177
## 6 0.9608 nan 0.1000 0.0154
## 7 0.9265 nan 0.1000 0.0121
## 8 0.8977 nan 0.1000 0.0086
## 9 0.8666 nan 0.1000 0.0122
## 10 0.8408 nan 0.1000 0.0113
## 20 0.6877 nan 0.1000 0.0009
## 40 0.5405 nan 0.1000 0.0007
## 60 0.4448 nan 0.1000 -0.0004
## 80 0.3753 nan 0.1000 -0.0005
## 100 0.3214 nan 0.1000 -0.0012
## 120 0.2741 nan 0.1000 0.0003
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## 180 0.1775 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2349 nan 0.1000 0.0394
## 2 1.1647 nan 0.1000 0.0297
## 3 1.1021 nan 0.1000 0.0290
## 4 1.0490 nan 0.1000 0.0249
## 5 1.0037 nan 0.1000 0.0196
## 6 0.9635 nan 0.1000 0.0151
## 7 0.9272 nan 0.1000 0.0135
## 8 0.8959 nan 0.1000 0.0123
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## 10 0.8446 nan 0.1000 0.0089
## 20 0.6826 nan 0.1000 0.0016
## 40 0.5282 nan 0.1000 -0.0014
## 60 0.4499 nan 0.1000 -0.0013
## 80 0.3851 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.1000 0.0346
## 2 1.1721 nan 0.1000 0.0253
## 3 1.1088 nan 0.1000 0.0282
## 4 1.0600 nan 0.1000 0.0185
## 5 1.0193 nan 0.1000 0.0160
## 6 0.9743 nan 0.1000 0.0195
## 7 0.9419 nan 0.1000 0.0129
## 8 0.9090 nan 0.1000 0.0108
## 9 0.8768 nan 0.1000 0.0127
## 10 0.8515 nan 0.1000 0.0094
## 20 0.6863 nan 0.1000 0.0011
## 40 0.5479 nan 0.1000 0.0000
## 60 0.4605 nan 0.1000 -0.0006
## 80 0.3956 nan 0.1000 -0.0017
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## 400 0.0591 nan 0.1000 -0.0002
## 420 0.0524 nan 0.1000 -0.0001
## 440 0.0477 nan 0.1000 -0.0001
## 460 0.0430 nan 0.1000 -0.0001
## 480 0.0386 nan 0.1000 -0.0001
## 500 0.0347 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2446 nan 0.1000 0.0313
## 2 1.1666 nan 0.1000 0.0366
## 3 1.1058 nan 0.1000 0.0253
## 4 1.0503 nan 0.1000 0.0184
## 5 0.9984 nan 0.1000 0.0225
## 6 0.9610 nan 0.1000 0.0163
## 7 0.9248 nan 0.1000 0.0142
## 8 0.8937 nan 0.1000 0.0121
## 9 0.8664 nan 0.1000 0.0082
## 10 0.8373 nan 0.1000 0.0134
## 20 0.6630 nan 0.1000 0.0017
## 40 0.4969 nan 0.1000 0.0015
## 60 0.3977 nan 0.1000 0.0007
## 80 0.3258 nan 0.1000 -0.0012
## 100 0.2724 nan 0.1000 0.0001
## 120 0.2302 nan 0.1000 -0.0005
## 140 0.1967 nan 0.1000 -0.0007
## 160 0.1673 nan 0.1000 -0.0004
## 180 0.1435 nan 0.1000 -0.0003
## 200 0.1211 nan 0.1000 -0.0003
## 220 0.1044 nan 0.1000 -0.0001
## 240 0.0911 nan 0.1000 -0.0001
## 260 0.0785 nan 0.1000 -0.0000
## 280 0.0701 nan 0.1000 -0.0003
## 300 0.0604 nan 0.1000 -0.0001
## 320 0.0520 nan 0.1000 -0.0000
## 340 0.0450 nan 0.1000 -0.0001
## 360 0.0385 nan 0.1000 -0.0001
## 380 0.0340 nan 0.1000 -0.0001
## 400 0.0303 nan 0.1000 -0.0001
## 420 0.0268 nan 0.1000 -0.0000
## 440 0.0235 nan 0.1000 0.0000
## 460 0.0210 nan 0.1000 -0.0000
## 480 0.0187 nan 0.1000 -0.0001
## 500 0.0166 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0376
## 2 1.1580 nan 0.1000 0.0343
## 3 1.0980 nan 0.1000 0.0255
## 4 1.0421 nan 0.1000 0.0262
## 5 0.9911 nan 0.1000 0.0217
## 6 0.9471 nan 0.1000 0.0189
## 7 0.9079 nan 0.1000 0.0174
## 8 0.8763 nan 0.1000 0.0116
## 9 0.8459 nan 0.1000 0.0112
## 10 0.8177 nan 0.1000 0.0092
## 20 0.6492 nan 0.1000 0.0028
## 40 0.4992 nan 0.1000 -0.0002
## 60 0.4036 nan 0.1000 -0.0012
## 80 0.3344 nan 0.1000 -0.0012
## 100 0.2751 nan 0.1000 -0.0009
## 120 0.2263 nan 0.1000 -0.0008
## 140 0.1943 nan 0.1000 -0.0002
## 160 0.1646 nan 0.1000 -0.0008
## 180 0.1420 nan 0.1000 -0.0005
## 200 0.1216 nan 0.1000 0.0001
## 220 0.1046 nan 0.1000 -0.0002
## 240 0.0907 nan 0.1000 -0.0004
## 260 0.0790 nan 0.1000 -0.0003
## 280 0.0693 nan 0.1000 -0.0002
## 300 0.0601 nan 0.1000 -0.0002
## 320 0.0526 nan 0.1000 -0.0002
## 340 0.0463 nan 0.1000 -0.0002
## 360 0.0408 nan 0.1000 -0.0003
## 380 0.0357 nan 0.1000 -0.0001
## 400 0.0316 nan 0.1000 -0.0001
## 420 0.0273 nan 0.1000 -0.0000
## 440 0.0239 nan 0.1000 -0.0001
## 460 0.0207 nan 0.1000 -0.0001
## 480 0.0185 nan 0.1000 -0.0000
## 500 0.0160 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2304 nan 0.1000 0.0442
## 2 1.1536 nan 0.1000 0.0362
## 3 1.0943 nan 0.1000 0.0225
## 4 1.0438 nan 0.1000 0.0247
## 5 0.9978 nan 0.1000 0.0213
## 6 0.9547 nan 0.1000 0.0164
## 7 0.9221 nan 0.1000 0.0144
## 8 0.8887 nan 0.1000 0.0135
## 9 0.8562 nan 0.1000 0.0139
## 10 0.8274 nan 0.1000 0.0112
## 20 0.6546 nan 0.1000 0.0021
## 40 0.5011 nan 0.1000 0.0011
## 60 0.4108 nan 0.1000 -0.0003
## 80 0.3401 nan 0.1000 -0.0002
## 100 0.2841 nan 0.1000 -0.0005
## 120 0.2397 nan 0.1000 -0.0007
## 140 0.2001 nan 0.1000 -0.0003
## 160 0.1720 nan 0.1000 -0.0005
## 180 0.1511 nan 0.1000 -0.0008
## 200 0.1296 nan 0.1000 -0.0006
## 220 0.1114 nan 0.1000 -0.0004
## 240 0.0960 nan 0.1000 -0.0000
## 260 0.0826 nan 0.1000 -0.0002
## 280 0.0710 nan 0.1000 -0.0003
## 300 0.0622 nan 0.1000 -0.0004
## 320 0.0546 nan 0.1000 -0.0001
## 340 0.0480 nan 0.1000 -0.0001
## 360 0.0422 nan 0.1000 -0.0002
## 380 0.0377 nan 0.1000 -0.0001
## 400 0.0330 nan 0.1000 -0.0001
## 420 0.0293 nan 0.1000 -0.0001
## 440 0.0256 nan 0.1000 -0.0001
## 460 0.0227 nan 0.1000 -0.0001
## 480 0.0202 nan 0.1000 -0.0000
## 500 0.0177 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0003
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2564 nan 0.0010 0.0003
## 100 1.2414 nan 0.0010 0.0003
## 120 1.2271 nan 0.0010 0.0003
## 140 1.2134 nan 0.0010 0.0003
## 160 1.2000 nan 0.0010 0.0003
## 180 1.1870 nan 0.0010 0.0003
## 200 1.1741 nan 0.0010 0.0002
## 220 1.1619 nan 0.0010 0.0002
## 240 1.1500 nan 0.0010 0.0002
## 260 1.1385 nan 0.0010 0.0003
## 280 1.1271 nan 0.0010 0.0003
## 300 1.1164 nan 0.0010 0.0002
## 320 1.1058 nan 0.0010 0.0002
## 340 1.0956 nan 0.0010 0.0002
## 360 1.0857 nan 0.0010 0.0002
## 380 1.0759 nan 0.0010 0.0002
## 400 1.0664 nan 0.0010 0.0002
## 420 1.0572 nan 0.0010 0.0002
## 440 1.0482 nan 0.0010 0.0002
## 460 1.0392 nan 0.0010 0.0002
## 480 1.0307 nan 0.0010 0.0002
## 500 1.0225 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0004
## 80 1.2563 nan 0.0010 0.0003
## 100 1.2413 nan 0.0010 0.0003
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2133 nan 0.0010 0.0003
## 160 1.1999 nan 0.0010 0.0003
## 180 1.1868 nan 0.0010 0.0003
## 200 1.1742 nan 0.0010 0.0002
## 220 1.1617 nan 0.0010 0.0003
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0002
## 280 1.1270 nan 0.0010 0.0003
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1056 nan 0.0010 0.0002
## 340 1.0952 nan 0.0010 0.0002
## 360 1.0852 nan 0.0010 0.0002
## 380 1.0755 nan 0.0010 0.0002
## 400 1.0660 nan 0.0010 0.0002
## 420 1.0568 nan 0.0010 0.0002
## 440 1.0477 nan 0.0010 0.0002
## 460 1.0390 nan 0.0010 0.0002
## 480 1.0308 nan 0.0010 0.0001
## 500 1.0225 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0003
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2562 nan 0.0010 0.0004
## 100 1.2410 nan 0.0010 0.0004
## 120 1.2271 nan 0.0010 0.0003
## 140 1.2132 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0002
## 180 1.1862 nan 0.0010 0.0003
## 200 1.1738 nan 0.0010 0.0003
## 220 1.1615 nan 0.0010 0.0002
## 240 1.1498 nan 0.0010 0.0003
## 260 1.1383 nan 0.0010 0.0003
## 280 1.1271 nan 0.0010 0.0003
## 300 1.1162 nan 0.0010 0.0002
## 320 1.1054 nan 0.0010 0.0002
## 340 1.0952 nan 0.0010 0.0002
## 360 1.0852 nan 0.0010 0.0002
## 380 1.0756 nan 0.0010 0.0002
## 400 1.0663 nan 0.0010 0.0002
## 420 1.0573 nan 0.0010 0.0002
## 440 1.0483 nan 0.0010 0.0002
## 460 1.0395 nan 0.0010 0.0002
## 480 1.0309 nan 0.0010 0.0002
## 500 1.0227 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0003
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0005
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2673 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2349 nan 0.0010 0.0004
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0003
## 180 1.1766 nan 0.0010 0.0003
## 200 1.1629 nan 0.0010 0.0003
## 220 1.1496 nan 0.0010 0.0003
## 240 1.1369 nan 0.0010 0.0003
## 260 1.1247 nan 0.0010 0.0003
## 280 1.1125 nan 0.0010 0.0003
## 300 1.1008 nan 0.0010 0.0003
## 320 1.0893 nan 0.0010 0.0002
## 340 1.0786 nan 0.0010 0.0002
## 360 1.0681 nan 0.0010 0.0002
## 380 1.0576 nan 0.0010 0.0002
## 400 1.0473 nan 0.0010 0.0002
## 420 1.0375 nan 0.0010 0.0002
## 440 1.0281 nan 0.0010 0.0002
## 460 1.0188 nan 0.0010 0.0002
## 480 1.0096 nan 0.0010 0.0002
## 500 1.0010 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0005
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2511 nan 0.0010 0.0003
## 100 1.2351 nan 0.0010 0.0003
## 120 1.2196 nan 0.0010 0.0004
## 140 1.2049 nan 0.0010 0.0003
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1770 nan 0.0010 0.0003
## 200 1.1635 nan 0.0010 0.0003
## 220 1.1504 nan 0.0010 0.0003
## 240 1.1380 nan 0.0010 0.0003
## 260 1.1258 nan 0.0010 0.0003
## 280 1.1139 nan 0.0010 0.0003
## 300 1.1023 nan 0.0010 0.0002
## 320 1.0912 nan 0.0010 0.0002
## 340 1.0800 nan 0.0010 0.0003
## 360 1.0693 nan 0.0010 0.0002
## 380 1.0588 nan 0.0010 0.0002
## 400 1.0487 nan 0.0010 0.0002
## 420 1.0388 nan 0.0010 0.0002
## 440 1.0294 nan 0.0010 0.0002
## 460 1.0204 nan 0.0010 0.0002
## 480 1.0113 nan 0.0010 0.0002
## 500 1.0026 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2519 nan 0.0010 0.0004
## 100 1.2362 nan 0.0010 0.0003
## 120 1.2212 nan 0.0010 0.0003
## 140 1.2062 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1777 nan 0.0010 0.0003
## 200 1.1644 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1389 nan 0.0010 0.0003
## 260 1.1266 nan 0.0010 0.0003
## 280 1.1149 nan 0.0010 0.0002
## 300 1.1035 nan 0.0010 0.0002
## 320 1.0924 nan 0.0010 0.0002
## 340 1.0817 nan 0.0010 0.0002
## 360 1.0712 nan 0.0010 0.0002
## 380 1.0608 nan 0.0010 0.0002
## 400 1.0506 nan 0.0010 0.0002
## 420 1.0411 nan 0.0010 0.0002
## 440 1.0316 nan 0.0010 0.0002
## 460 1.0223 nan 0.0010 0.0002
## 480 1.0132 nan 0.0010 0.0002
## 500 1.0045 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0004
## 3 1.3176 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3156 nan 0.0010 0.0005
## 6 1.3146 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3117 nan 0.0010 0.0005
## 10 1.3107 nan 0.0010 0.0004
## 20 1.3012 nan 0.0010 0.0004
## 40 1.2827 nan 0.0010 0.0004
## 60 1.2648 nan 0.0010 0.0004
## 80 1.2476 nan 0.0010 0.0004
## 100 1.2312 nan 0.0010 0.0003
## 120 1.2154 nan 0.0010 0.0003
## 140 1.1997 nan 0.0010 0.0004
## 160 1.1848 nan 0.0010 0.0004
## 180 1.1705 nan 0.0010 0.0003
## 200 1.1563 nan 0.0010 0.0003
## 220 1.1424 nan 0.0010 0.0003
## 240 1.1291 nan 0.0010 0.0003
## 260 1.1162 nan 0.0010 0.0003
## 280 1.1037 nan 0.0010 0.0003
## 300 1.0915 nan 0.0010 0.0003
## 320 1.0796 nan 0.0010 0.0002
## 340 1.0682 nan 0.0010 0.0003
## 360 1.0569 nan 0.0010 0.0003
## 380 1.0462 nan 0.0010 0.0002
## 400 1.0358 nan 0.0010 0.0002
## 420 1.0254 nan 0.0010 0.0002
## 440 1.0154 nan 0.0010 0.0002
## 460 1.0057 nan 0.0010 0.0002
## 480 0.9962 nan 0.0010 0.0002
## 500 0.9872 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3186 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0005
## 20 1.3016 nan 0.0010 0.0004
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2479 nan 0.0010 0.0003
## 100 1.2314 nan 0.0010 0.0004
## 120 1.2156 nan 0.0010 0.0003
## 140 1.2000 nan 0.0010 0.0003
## 160 1.1849 nan 0.0010 0.0004
## 180 1.1702 nan 0.0010 0.0003
## 200 1.1564 nan 0.0010 0.0003
## 220 1.1429 nan 0.0010 0.0003
## 240 1.1298 nan 0.0010 0.0003
## 260 1.1171 nan 0.0010 0.0003
## 280 1.1048 nan 0.0010 0.0002
## 300 1.0926 nan 0.0010 0.0003
## 320 1.0810 nan 0.0010 0.0003
## 340 1.0695 nan 0.0010 0.0002
## 360 1.0585 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0002
## 400 1.0376 nan 0.0010 0.0002
## 420 1.0273 nan 0.0010 0.0002
## 440 1.0175 nan 0.0010 0.0002
## 460 1.0076 nan 0.0010 0.0002
## 480 0.9983 nan 0.0010 0.0002
## 500 0.9891 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0005
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2663 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0004
## 100 1.2329 nan 0.0010 0.0004
## 120 1.2170 nan 0.0010 0.0004
## 140 1.2017 nan 0.0010 0.0003
## 160 1.1868 nan 0.0010 0.0003
## 180 1.1726 nan 0.0010 0.0003
## 200 1.1584 nan 0.0010 0.0003
## 220 1.1449 nan 0.0010 0.0003
## 240 1.1316 nan 0.0010 0.0003
## 260 1.1188 nan 0.0010 0.0003
## 280 1.1062 nan 0.0010 0.0003
## 300 1.0944 nan 0.0010 0.0002
## 320 1.0827 nan 0.0010 0.0002
## 340 1.0716 nan 0.0010 0.0003
## 360 1.0606 nan 0.0010 0.0002
## 380 1.0499 nan 0.0010 0.0002
## 400 1.0397 nan 0.0010 0.0002
## 420 1.0297 nan 0.0010 0.0003
## 440 1.0201 nan 0.0010 0.0002
## 460 1.0107 nan 0.0010 0.0002
## 480 1.0012 nan 0.0010 0.0002
## 500 0.9921 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0037
## 2 1.3028 nan 0.0100 0.0042
## 3 1.2944 nan 0.0100 0.0037
## 4 1.2863 nan 0.0100 0.0039
## 5 1.2785 nan 0.0100 0.0035
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2620 nan 0.0100 0.0034
## 8 1.2548 nan 0.0100 0.0029
## 9 1.2475 nan 0.0100 0.0030
## 10 1.2408 nan 0.0100 0.0030
## 20 1.1745 nan 0.0100 0.0027
## 40 1.0667 nan 0.0100 0.0020
## 60 0.9843 nan 0.0100 0.0015
## 80 0.9185 nan 0.0100 0.0011
## 100 0.8660 nan 0.0100 0.0010
## 120 0.8224 nan 0.0100 0.0007
## 140 0.7855 nan 0.0100 0.0006
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## 180 0.7299 nan 0.0100 0.0004
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## 220 0.6859 nan 0.0100 0.0003
## 240 0.6689 nan 0.0100 0.0002
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## 320 0.6112 nan 0.0100 0.0002
## 340 0.6002 nan 0.0100 0.0001
## 360 0.5892 nan 0.0100 0.0001
## 380 0.5788 nan 0.0100 0.0001
## 400 0.5693 nan 0.0100 -0.0000
## 420 0.5600 nan 0.0100 0.0001
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## 460 0.5417 nan 0.0100 0.0001
## 480 0.5334 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0042
## 2 1.3033 nan 0.0100 0.0037
## 3 1.2952 nan 0.0100 0.0035
## 4 1.2875 nan 0.0100 0.0035
## 5 1.2792 nan 0.0100 0.0035
## 6 1.2712 nan 0.0100 0.0037
## 7 1.2641 nan 0.0100 0.0032
## 8 1.2571 nan 0.0100 0.0029
## 9 1.2495 nan 0.0100 0.0034
## 10 1.2417 nan 0.0100 0.0036
## 20 1.1743 nan 0.0100 0.0026
## 40 1.0650 nan 0.0100 0.0021
## 60 0.9829 nan 0.0100 0.0012
## 80 0.9197 nan 0.0100 0.0009
## 100 0.8659 nan 0.0100 0.0010
## 120 0.8225 nan 0.0100 0.0007
## 140 0.7873 nan 0.0100 0.0004
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## 180 0.7333 nan 0.0100 0.0003
## 200 0.7117 nan 0.0100 -0.0000
## 220 0.6912 nan 0.0100 0.0002
## 240 0.6743 nan 0.0100 0.0001
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## 280 0.6456 nan 0.0100 -0.0001
## 300 0.6329 nan 0.0100 0.0001
## 320 0.6208 nan 0.0100 -0.0002
## 340 0.6086 nan 0.0100 0.0002
## 360 0.5982 nan 0.0100 0.0001
## 380 0.5875 nan 0.0100 0.0000
## 400 0.5780 nan 0.0100 0.0000
## 420 0.5690 nan 0.0100 -0.0001
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## 460 0.5510 nan 0.0100 0.0001
## 480 0.5426 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0042
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2954 nan 0.0100 0.0038
## 4 1.2878 nan 0.0100 0.0038
## 5 1.2791 nan 0.0100 0.0039
## 6 1.2711 nan 0.0100 0.0036
## 7 1.2630 nan 0.0100 0.0038
## 8 1.2549 nan 0.0100 0.0034
## 9 1.2475 nan 0.0100 0.0035
## 10 1.2399 nan 0.0100 0.0031
## 20 1.1725 nan 0.0100 0.0029
## 40 1.0655 nan 0.0100 0.0020
## 60 0.9843 nan 0.0100 0.0016
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## 140 0.7905 nan 0.0100 0.0003
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## 240 0.6779 nan 0.0100 0.0003
## 260 0.6624 nan 0.0100 -0.0000
## 280 0.6484 nan 0.0100 0.0000
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## 320 0.6237 nan 0.0100 0.0000
## 340 0.6121 nan 0.0100 0.0000
## 360 0.6012 nan 0.0100 -0.0000
## 380 0.5911 nan 0.0100 0.0001
## 400 0.5818 nan 0.0100 -0.0001
## 420 0.5733 nan 0.0100 -0.0000
## 440 0.5644 nan 0.0100 -0.0000
## 460 0.5566 nan 0.0100 -0.0000
## 480 0.5482 nan 0.0100 -0.0000
## 500 0.5399 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0041
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2959 nan 0.0100 0.0039
## 4 1.2874 nan 0.0100 0.0041
## 5 1.2793 nan 0.0100 0.0036
## 6 1.2701 nan 0.0100 0.0043
## 7 1.2630 nan 0.0100 0.0034
## 8 1.2545 nan 0.0100 0.0035
## 9 1.2469 nan 0.0100 0.0033
## 10 1.2384 nan 0.0100 0.0039
## 20 1.1639 nan 0.0100 0.0028
## 40 1.0483 nan 0.0100 0.0024
## 60 0.9616 nan 0.0100 0.0013
## 80 0.8919 nan 0.0100 0.0012
## 100 0.8371 nan 0.0100 0.0007
## 120 0.7926 nan 0.0100 0.0007
## 140 0.7541 nan 0.0100 0.0005
## 160 0.7217 nan 0.0100 0.0001
## 180 0.6935 nan 0.0100 0.0005
## 200 0.6706 nan 0.0100 0.0004
## 220 0.6496 nan 0.0100 0.0002
## 240 0.6303 nan 0.0100 0.0002
## 260 0.6126 nan 0.0100 0.0002
## 280 0.5961 nan 0.0100 0.0001
## 300 0.5813 nan 0.0100 0.0001
## 320 0.5673 nan 0.0100 -0.0001
## 340 0.5549 nan 0.0100 -0.0001
## 360 0.5426 nan 0.0100 0.0002
## 380 0.5307 nan 0.0100 0.0001
## 400 0.5198 nan 0.0100 -0.0003
## 420 0.5087 nan 0.0100 -0.0000
## 440 0.4978 nan 0.0100 -0.0000
## 460 0.4878 nan 0.0100 0.0001
## 480 0.4789 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3028 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2851 nan 0.0100 0.0036
## 5 1.2762 nan 0.0100 0.0038
## 6 1.2686 nan 0.0100 0.0034
## 7 1.2605 nan 0.0100 0.0034
## 8 1.2525 nan 0.0100 0.0032
## 9 1.2448 nan 0.0100 0.0035
## 10 1.2366 nan 0.0100 0.0038
## 20 1.1634 nan 0.0100 0.0034
## 40 1.0482 nan 0.0100 0.0023
## 60 0.9630 nan 0.0100 0.0015
## 80 0.8936 nan 0.0100 0.0011
## 100 0.8385 nan 0.0100 0.0011
## 120 0.7945 nan 0.0100 0.0008
## 140 0.7578 nan 0.0100 0.0004
## 160 0.7272 nan 0.0100 0.0007
## 180 0.7007 nan 0.0100 0.0002
## 200 0.6779 nan 0.0100 0.0001
## 220 0.6553 nan 0.0100 0.0004
## 240 0.6357 nan 0.0100 0.0003
## 260 0.6189 nan 0.0100 0.0002
## 280 0.6036 nan 0.0100 0.0000
## 300 0.5904 nan 0.0100 0.0002
## 320 0.5770 nan 0.0100 0.0001
## 340 0.5644 nan 0.0100 0.0000
## 360 0.5534 nan 0.0100 0.0001
## 380 0.5424 nan 0.0100 0.0000
## 400 0.5323 nan 0.0100 -0.0001
## 420 0.5219 nan 0.0100 -0.0000
## 440 0.5120 nan 0.0100 0.0000
## 460 0.5016 nan 0.0100 -0.0001
## 480 0.4926 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0040
## 3 1.2938 nan 0.0100 0.0041
## 4 1.2853 nan 0.0100 0.0035
## 5 1.2761 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0039
## 7 1.2598 nan 0.0100 0.0041
## 8 1.2534 nan 0.0100 0.0025
## 9 1.2453 nan 0.0100 0.0035
## 10 1.2375 nan 0.0100 0.0034
## 20 1.1671 nan 0.0100 0.0032
## 40 1.0523 nan 0.0100 0.0022
## 60 0.9657 nan 0.0100 0.0018
## 80 0.8975 nan 0.0100 0.0016
## 100 0.8427 nan 0.0100 0.0010
## 120 0.7978 nan 0.0100 0.0007
## 140 0.7609 nan 0.0100 0.0003
## 160 0.7308 nan 0.0100 0.0004
## 180 0.7046 nan 0.0100 0.0002
## 200 0.6823 nan 0.0100 0.0001
## 220 0.6624 nan 0.0100 0.0002
## 240 0.6449 nan 0.0100 0.0002
## 260 0.6284 nan 0.0100 -0.0001
## 280 0.6141 nan 0.0100 -0.0000
## 300 0.5997 nan 0.0100 -0.0000
## 320 0.5862 nan 0.0100 0.0000
## 340 0.5742 nan 0.0100 0.0000
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## 380 0.5505 nan 0.0100 0.0001
## 400 0.5395 nan 0.0100 0.0000
## 420 0.5289 nan 0.0100 0.0000
## 440 0.5200 nan 0.0100 0.0001
## 460 0.5110 nan 0.0100 -0.0002
## 480 0.5013 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0044
## 2 1.3015 nan 0.0100 0.0042
## 3 1.2925 nan 0.0100 0.0042
## 4 1.2834 nan 0.0100 0.0038
## 5 1.2741 nan 0.0100 0.0042
## 6 1.2650 nan 0.0100 0.0038
## 7 1.2571 nan 0.0100 0.0035
## 8 1.2481 nan 0.0100 0.0040
## 9 1.2393 nan 0.0100 0.0041
## 10 1.2304 nan 0.0100 0.0039
## 20 1.1535 nan 0.0100 0.0029
## 40 1.0320 nan 0.0100 0.0023
## 60 0.9427 nan 0.0100 0.0017
## 80 0.8725 nan 0.0100 0.0011
## 100 0.8125 nan 0.0100 0.0009
## 120 0.7670 nan 0.0100 0.0007
## 140 0.7291 nan 0.0100 0.0006
## 160 0.6942 nan 0.0100 0.0003
## 180 0.6655 nan 0.0100 0.0003
## 200 0.6399 nan 0.0100 0.0002
## 220 0.6175 nan 0.0100 0.0002
## 240 0.5989 nan 0.0100 0.0001
## 260 0.5805 nan 0.0100 0.0003
## 280 0.5633 nan 0.0100 0.0000
## 300 0.5475 nan 0.0100 0.0001
## 320 0.5330 nan 0.0100 0.0002
## 340 0.5195 nan 0.0100 -0.0001
## 360 0.5062 nan 0.0100 0.0000
## 380 0.4934 nan 0.0100 -0.0000
## 400 0.4815 nan 0.0100 0.0000
## 420 0.4701 nan 0.0100 0.0001
## 440 0.4591 nan 0.0100 -0.0000
## 460 0.4484 nan 0.0100 0.0000
## 480 0.4386 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3024 nan 0.0100 0.0038
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2850 nan 0.0100 0.0043
## 5 1.2768 nan 0.0100 0.0038
## 6 1.2684 nan 0.0100 0.0039
## 7 1.2601 nan 0.0100 0.0034
## 8 1.2517 nan 0.0100 0.0037
## 9 1.2429 nan 0.0100 0.0039
## 10 1.2359 nan 0.0100 0.0031
## 20 1.1588 nan 0.0100 0.0031
## 40 1.0375 nan 0.0100 0.0023
## 60 0.9466 nan 0.0100 0.0018
## 80 0.8756 nan 0.0100 0.0013
## 100 0.8188 nan 0.0100 0.0011
## 120 0.7723 nan 0.0100 0.0006
## 140 0.7322 nan 0.0100 0.0005
## 160 0.6997 nan 0.0100 0.0001
## 180 0.6712 nan 0.0100 0.0006
## 200 0.6475 nan 0.0100 -0.0000
## 220 0.6262 nan 0.0100 0.0003
## 240 0.6063 nan 0.0100 0.0001
## 260 0.5877 nan 0.0100 0.0000
## 280 0.5707 nan 0.0100 -0.0000
## 300 0.5546 nan 0.0100 0.0002
## 320 0.5404 nan 0.0100 0.0000
## 340 0.5263 nan 0.0100 -0.0000
## 360 0.5134 nan 0.0100 -0.0002
## 380 0.5004 nan 0.0100 0.0001
## 400 0.4891 nan 0.0100 -0.0000
## 420 0.4783 nan 0.0100 0.0000
## 440 0.4670 nan 0.0100 -0.0001
## 460 0.4569 nan 0.0100 -0.0000
## 480 0.4470 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0037
## 4 1.2850 nan 0.0100 0.0042
## 5 1.2766 nan 0.0100 0.0035
## 6 1.2673 nan 0.0100 0.0041
## 7 1.2584 nan 0.0100 0.0038
## 8 1.2503 nan 0.0100 0.0037
## 9 1.2416 nan 0.0100 0.0040
## 10 1.2333 nan 0.0100 0.0035
## 20 1.1588 nan 0.0100 0.0025
## 40 1.0415 nan 0.0100 0.0022
## 60 0.9506 nan 0.0100 0.0013
## 80 0.8795 nan 0.0100 0.0013
## 100 0.8227 nan 0.0100 0.0009
## 120 0.7769 nan 0.0100 0.0006
## 140 0.7405 nan 0.0100 0.0004
## 160 0.7085 nan 0.0100 0.0003
## 180 0.6803 nan 0.0100 0.0003
## 200 0.6557 nan 0.0100 0.0004
## 220 0.6354 nan 0.0100 0.0001
## 240 0.6158 nan 0.0100 -0.0001
## 260 0.5976 nan 0.0100 0.0000
## 280 0.5815 nan 0.0100 -0.0000
## 300 0.5665 nan 0.0100 0.0000
## 320 0.5530 nan 0.0100 0.0001
## 340 0.5387 nan 0.0100 0.0000
## 360 0.5250 nan 0.0100 0.0001
## 380 0.5142 nan 0.0100 -0.0001
## 400 0.5030 nan 0.0100 -0.0000
## 420 0.4918 nan 0.0100 -0.0000
## 440 0.4821 nan 0.0100 -0.0001
## 460 0.4723 nan 0.0100 0.0001
## 480 0.4622 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2390 nan 0.1000 0.0380
## 2 1.1665 nan 0.1000 0.0319
## 3 1.1073 nan 0.1000 0.0276
## 4 1.0578 nan 0.1000 0.0233
## 5 1.0142 nan 0.1000 0.0174
## 6 0.9747 nan 0.1000 0.0176
## 7 0.9387 nan 0.1000 0.0154
## 8 0.9123 nan 0.1000 0.0097
## 9 0.8800 nan 0.1000 0.0125
## 10 0.8553 nan 0.1000 0.0080
## 20 0.7077 nan 0.1000 0.0027
## 40 0.5731 nan 0.1000 0.0009
## 60 0.4900 nan 0.1000 0.0001
## 80 0.4305 nan 0.1000 -0.0009
## 100 0.3739 nan 0.1000 -0.0001
## 120 0.3309 nan 0.1000 -0.0004
## 140 0.2939 nan 0.1000 -0.0008
## 160 0.2636 nan 0.1000 -0.0009
## 180 0.2338 nan 0.1000 -0.0000
## 200 0.2118 nan 0.1000 -0.0008
## 220 0.1925 nan 0.1000 -0.0008
## 240 0.1745 nan 0.1000 -0.0002
## 260 0.1603 nan 0.1000 -0.0003
## 280 0.1469 nan 0.1000 -0.0003
## 300 0.1329 nan 0.1000 -0.0002
## 320 0.1216 nan 0.1000 -0.0005
## 340 0.1108 nan 0.1000 -0.0002
## 360 0.1004 nan 0.1000 -0.0001
## 380 0.0921 nan 0.1000 -0.0004
## 400 0.0853 nan 0.1000 -0.0003
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## 440 0.0716 nan 0.1000 -0.0002
## 460 0.0666 nan 0.1000 -0.0002
## 480 0.0613 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0412
## 2 1.1689 nan 0.1000 0.0320
## 3 1.1105 nan 0.1000 0.0250
## 4 1.0586 nan 0.1000 0.0243
## 5 1.0181 nan 0.1000 0.0192
## 6 0.9777 nan 0.1000 0.0170
## 7 0.9444 nan 0.1000 0.0135
## 8 0.9154 nan 0.1000 0.0115
## 9 0.8888 nan 0.1000 0.0108
## 10 0.8663 nan 0.1000 0.0094
## 20 0.7118 nan 0.1000 0.0033
## 40 0.5855 nan 0.1000 -0.0007
## 60 0.4978 nan 0.1000 0.0006
## 80 0.4275 nan 0.1000 -0.0014
## 100 0.3774 nan 0.1000 -0.0004
## 120 0.3343 nan 0.1000 -0.0009
## 140 0.3007 nan 0.1000 -0.0009
## 160 0.2708 nan 0.1000 -0.0003
## 180 0.2468 nan 0.1000 -0.0004
## 200 0.2220 nan 0.1000 -0.0004
## 220 0.2044 nan 0.1000 -0.0005
## 240 0.1848 nan 0.1000 -0.0007
## 260 0.1676 nan 0.1000 -0.0005
## 280 0.1525 nan 0.1000 -0.0003
## 300 0.1396 nan 0.1000 -0.0004
## 320 0.1273 nan 0.1000 -0.0003
## 340 0.1163 nan 0.1000 -0.0003
## 360 0.1079 nan 0.1000 -0.0002
## 380 0.0992 nan 0.1000 -0.0002
## 400 0.0920 nan 0.1000 -0.0003
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## 440 0.0771 nan 0.1000 -0.0001
## 460 0.0704 nan 0.1000 -0.0002
## 480 0.0651 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0333
## 2 1.1719 nan 0.1000 0.0303
## 3 1.1162 nan 0.1000 0.0259
## 4 1.0687 nan 0.1000 0.0211
## 5 1.0250 nan 0.1000 0.0177
## 6 0.9855 nan 0.1000 0.0155
## 7 0.9543 nan 0.1000 0.0133
## 8 0.9224 nan 0.1000 0.0141
## 9 0.8965 nan 0.1000 0.0093
## 10 0.8699 nan 0.1000 0.0072
## 20 0.7147 nan 0.1000 0.0016
## 40 0.5858 nan 0.1000 -0.0009
## 60 0.5076 nan 0.1000 0.0007
## 80 0.4488 nan 0.1000 -0.0008
## 100 0.4018 nan 0.1000 -0.0015
## 120 0.3611 nan 0.1000 -0.0001
## 140 0.3204 nan 0.1000 -0.0006
## 160 0.2904 nan 0.1000 -0.0005
## 180 0.2645 nan 0.1000 -0.0003
## 200 0.2405 nan 0.1000 -0.0007
## 220 0.2211 nan 0.1000 -0.0009
## 240 0.2000 nan 0.1000 -0.0006
## 260 0.1821 nan 0.1000 -0.0004
## 280 0.1678 nan 0.1000 -0.0003
## 300 0.1559 nan 0.1000 -0.0007
## 320 0.1430 nan 0.1000 -0.0003
## 340 0.1316 nan 0.1000 -0.0004
## 360 0.1214 nan 0.1000 -0.0006
## 380 0.1110 nan 0.1000 -0.0003
## 400 0.1032 nan 0.1000 -0.0006
## 420 0.0954 nan 0.1000 -0.0001
## 440 0.0884 nan 0.1000 -0.0001
## 460 0.0818 nan 0.1000 -0.0002
## 480 0.0755 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0430
## 2 1.1588 nan 0.1000 0.0289
## 3 1.0998 nan 0.1000 0.0236
## 4 1.0497 nan 0.1000 0.0222
## 5 1.0015 nan 0.1000 0.0186
## 6 0.9591 nan 0.1000 0.0180
## 7 0.9186 nan 0.1000 0.0154
## 8 0.8886 nan 0.1000 0.0107
## 9 0.8656 nan 0.1000 0.0094
## 10 0.8408 nan 0.1000 0.0088
## 20 0.6858 nan 0.1000 -0.0001
## 40 0.5346 nan 0.1000 0.0001
## 60 0.4391 nan 0.1000 -0.0006
## 80 0.3686 nan 0.1000 -0.0012
## 100 0.3193 nan 0.1000 -0.0000
## 120 0.2781 nan 0.1000 -0.0006
## 140 0.2422 nan 0.1000 -0.0003
## 160 0.2120 nan 0.1000 -0.0009
## 180 0.1864 nan 0.1000 -0.0003
## 200 0.1643 nan 0.1000 0.0002
## 220 0.1464 nan 0.1000 -0.0002
## 240 0.1292 nan 0.1000 -0.0000
## 260 0.1153 nan 0.1000 -0.0000
## 280 0.1020 nan 0.1000 -0.0003
## 300 0.0910 nan 0.1000 -0.0003
## 320 0.0813 nan 0.1000 -0.0003
## 340 0.0729 nan 0.1000 -0.0001
## 360 0.0660 nan 0.1000 -0.0000
## 380 0.0593 nan 0.1000 -0.0001
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## 480 0.0363 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0386
## 2 1.1734 nan 0.1000 0.0307
## 3 1.1095 nan 0.1000 0.0257
## 4 1.0490 nan 0.1000 0.0246
## 5 1.0059 nan 0.1000 0.0176
## 6 0.9661 nan 0.1000 0.0159
## 7 0.9243 nan 0.1000 0.0182
## 8 0.8936 nan 0.1000 0.0117
## 9 0.8665 nan 0.1000 0.0107
## 10 0.8453 nan 0.1000 0.0079
## 20 0.6894 nan 0.1000 0.0037
## 40 0.5419 nan 0.1000 0.0010
## 60 0.4525 nan 0.1000 -0.0012
## 80 0.3904 nan 0.1000 -0.0015
## 100 0.3337 nan 0.1000 -0.0006
## 120 0.2854 nan 0.1000 -0.0010
## 140 0.2528 nan 0.1000 -0.0008
## 160 0.2218 nan 0.1000 -0.0001
## 180 0.1954 nan 0.1000 -0.0011
## 200 0.1702 nan 0.1000 -0.0003
## 220 0.1499 nan 0.1000 -0.0004
## 240 0.1330 nan 0.1000 -0.0005
## 260 0.1181 nan 0.1000 -0.0002
## 280 0.1062 nan 0.1000 -0.0004
## 300 0.0939 nan 0.1000 -0.0001
## 320 0.0844 nan 0.1000 -0.0001
## 340 0.0757 nan 0.1000 -0.0001
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## 380 0.0613 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2307 nan 0.1000 0.0411
## 2 1.1535 nan 0.1000 0.0329
## 3 1.0868 nan 0.1000 0.0313
## 4 1.0425 nan 0.1000 0.0175
## 5 0.9927 nan 0.1000 0.0222
## 6 0.9541 nan 0.1000 0.0155
## 7 0.9208 nan 0.1000 0.0111
## 8 0.8895 nan 0.1000 0.0102
## 9 0.8605 nan 0.1000 0.0114
## 10 0.8381 nan 0.1000 0.0080
## 20 0.6784 nan 0.1000 0.0005
## 40 0.5425 nan 0.1000 -0.0001
## 60 0.4600 nan 0.1000 -0.0002
## 80 0.3838 nan 0.1000 -0.0009
## 100 0.3312 nan 0.1000 -0.0002
## 120 0.2857 nan 0.1000 -0.0006
## 140 0.2531 nan 0.1000 -0.0006
## 160 0.2224 nan 0.1000 -0.0011
## 180 0.1965 nan 0.1000 -0.0001
## 200 0.1739 nan 0.1000 -0.0004
## 220 0.1561 nan 0.1000 -0.0006
## 240 0.1391 nan 0.1000 -0.0009
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## 280 0.1112 nan 0.1000 -0.0004
## 300 0.1004 nan 0.1000 -0.0002
## 320 0.0897 nan 0.1000 -0.0003
## 340 0.0798 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.1000 0.0421
## 2 1.1499 nan 0.1000 0.0300
## 3 1.0932 nan 0.1000 0.0243
## 4 1.0371 nan 0.1000 0.0266
## 5 0.9873 nan 0.1000 0.0198
## 6 0.9508 nan 0.1000 0.0134
## 7 0.9084 nan 0.1000 0.0179
## 8 0.8696 nan 0.1000 0.0148
## 9 0.8389 nan 0.1000 0.0116
## 10 0.8166 nan 0.1000 0.0085
## 20 0.6429 nan 0.1000 -0.0003
## 40 0.4837 nan 0.1000 -0.0001
## 60 0.3937 nan 0.1000 -0.0002
## 80 0.3186 nan 0.1000 -0.0012
## 100 0.2702 nan 0.1000 -0.0006
## 120 0.2227 nan 0.1000 -0.0004
## 140 0.1881 nan 0.1000 -0.0003
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## 180 0.1381 nan 0.1000 -0.0004
## 200 0.1211 nan 0.1000 -0.0001
## 220 0.1044 nan 0.1000 -0.0004
## 240 0.0914 nan 0.1000 -0.0002
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## 280 0.0697 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2195 nan 0.1000 0.0448
## 2 1.1484 nan 0.1000 0.0369
## 3 1.0880 nan 0.1000 0.0233
## 4 1.0278 nan 0.1000 0.0232
## 5 0.9807 nan 0.1000 0.0216
## 6 0.9381 nan 0.1000 0.0171
## 7 0.9073 nan 0.1000 0.0133
## 8 0.8744 nan 0.1000 0.0123
## 9 0.8469 nan 0.1000 0.0114
## 10 0.8215 nan 0.1000 0.0106
## 20 0.6548 nan 0.1000 0.0032
## 40 0.4957 nan 0.1000 -0.0002
## 60 0.4047 nan 0.1000 -0.0011
## 80 0.3350 nan 0.1000 -0.0006
## 100 0.2772 nan 0.1000 0.0004
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## 140 0.1944 nan 0.1000 -0.0011
## 160 0.1683 nan 0.1000 -0.0000
## 180 0.1438 nan 0.1000 -0.0005
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## 220 0.1083 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2224 nan 0.1000 0.0457
## 2 1.1503 nan 0.1000 0.0303
## 3 1.0878 nan 0.1000 0.0276
## 4 1.0377 nan 0.1000 0.0234
## 5 0.9947 nan 0.1000 0.0189
## 6 0.9563 nan 0.1000 0.0163
## 7 0.9160 nan 0.1000 0.0170
## 8 0.8867 nan 0.1000 0.0112
## 9 0.8554 nan 0.1000 0.0125
## 10 0.8264 nan 0.1000 0.0111
## 20 0.6587 nan 0.1000 0.0023
## 40 0.5114 nan 0.1000 -0.0014
## 60 0.4074 nan 0.1000 -0.0007
## 80 0.3376 nan 0.1000 0.0001
## 100 0.2861 nan 0.1000 -0.0009
## 120 0.2463 nan 0.1000 -0.0012
## 140 0.2117 nan 0.1000 -0.0007
## 160 0.1808 nan 0.1000 -0.0009
## 180 0.1555 nan 0.1000 -0.0004
## 200 0.1339 nan 0.1000 -0.0006
## 220 0.1147 nan 0.1000 -0.0002
## 240 0.0991 nan 0.1000 -0.0006
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## 380 0.0407 nan 0.1000 -0.0000
## 400 0.0361 nan 0.1000 -0.0002
## 420 0.0318 nan 0.1000 -0.0001
## 440 0.0281 nan 0.1000 -0.0000
## 460 0.0251 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2880 nan 0.0010 0.0003
## 60 1.2723 nan 0.0010 0.0004
## 80 1.2574 nan 0.0010 0.0003
## 100 1.2430 nan 0.0010 0.0003
## 120 1.2288 nan 0.0010 0.0003
## 140 1.2153 nan 0.0010 0.0003
## 160 1.2018 nan 0.0010 0.0003
## 180 1.1895 nan 0.0010 0.0002
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## 320 1.1103 nan 0.0010 0.0002
## 340 1.1001 nan 0.0010 0.0002
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## 380 1.0807 nan 0.0010 0.0002
## 400 1.0717 nan 0.0010 0.0002
## 420 1.0627 nan 0.0010 0.0002
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## 460 1.0455 nan 0.0010 0.0002
## 480 1.0371 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0004
## 9 1.3137 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2878 nan 0.0010 0.0004
## 60 1.2720 nan 0.0010 0.0003
## 80 1.2569 nan 0.0010 0.0004
## 100 1.2426 nan 0.0010 0.0004
## 120 1.2287 nan 0.0010 0.0003
## 140 1.2148 nan 0.0010 0.0003
## 160 1.2016 nan 0.0010 0.0002
## 180 1.1891 nan 0.0010 0.0003
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## 220 1.1650 nan 0.0010 0.0002
## 240 1.1533 nan 0.0010 0.0003
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## 280 1.1310 nan 0.0010 0.0002
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## 320 1.1099 nan 0.0010 0.0002
## 340 1.1000 nan 0.0010 0.0002
## 360 1.0903 nan 0.0010 0.0002
## 380 1.0807 nan 0.0010 0.0002
## 400 1.0718 nan 0.0010 0.0002
## 420 1.0629 nan 0.0010 0.0002
## 440 1.0541 nan 0.0010 0.0002
## 460 1.0456 nan 0.0010 0.0002
## 480 1.0373 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0003
## 80 1.2581 nan 0.0010 0.0004
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2293 nan 0.0010 0.0003
## 140 1.2159 nan 0.0010 0.0003
## 160 1.2027 nan 0.0010 0.0003
## 180 1.1898 nan 0.0010 0.0003
## 200 1.1779 nan 0.0010 0.0002
## 220 1.1662 nan 0.0010 0.0003
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## 380 1.0825 nan 0.0010 0.0002
## 400 1.0735 nan 0.0010 0.0002
## 420 1.0646 nan 0.0010 0.0002
## 440 1.0559 nan 0.0010 0.0002
## 460 1.0474 nan 0.0010 0.0002
## 480 1.0392 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2690 nan 0.0010 0.0004
## 80 1.2528 nan 0.0010 0.0003
## 100 1.2375 nan 0.0010 0.0004
## 120 1.2227 nan 0.0010 0.0003
## 140 1.2080 nan 0.0010 0.0003
## 160 1.1942 nan 0.0010 0.0003
## 180 1.1805 nan 0.0010 0.0003
## 200 1.1672 nan 0.0010 0.0003
## 220 1.1544 nan 0.0010 0.0003
## 240 1.1420 nan 0.0010 0.0003
## 260 1.1299 nan 0.0010 0.0002
## 280 1.1185 nan 0.0010 0.0003
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## 320 1.0963 nan 0.0010 0.0002
## 340 1.0857 nan 0.0010 0.0002
## 360 1.0752 nan 0.0010 0.0002
## 380 1.0654 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0461 nan 0.0010 0.0002
## 440 1.0371 nan 0.0010 0.0002
## 460 1.0280 nan 0.0010 0.0002
## 480 1.0195 nan 0.0010 0.0002
## 500 1.0111 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0003
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2541 nan 0.0010 0.0003
## 100 1.2387 nan 0.0010 0.0003
## 120 1.2237 nan 0.0010 0.0003
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1958 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1566 nan 0.0010 0.0003
## 240 1.1445 nan 0.0010 0.0002
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1211 nan 0.0010 0.0002
## 300 1.1098 nan 0.0010 0.0002
## 320 1.0989 nan 0.0010 0.0002
## 340 1.0885 nan 0.0010 0.0002
## 360 1.0781 nan 0.0010 0.0002
## 380 1.0682 nan 0.0010 0.0002
## 400 1.0584 nan 0.0010 0.0002
## 420 1.0490 nan 0.0010 0.0002
## 440 1.0399 nan 0.0010 0.0002
## 460 1.0309 nan 0.0010 0.0002
## 480 1.0222 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2703 nan 0.0010 0.0004
## 80 1.2546 nan 0.0010 0.0004
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2248 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1699 nan 0.0010 0.0003
## 220 1.1573 nan 0.0010 0.0003
## 240 1.1451 nan 0.0010 0.0003
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## 280 1.1216 nan 0.0010 0.0003
## 300 1.1105 nan 0.0010 0.0003
## 320 1.0999 nan 0.0010 0.0002
## 340 1.0894 nan 0.0010 0.0002
## 360 1.0794 nan 0.0010 0.0002
## 380 1.0694 nan 0.0010 0.0002
## 400 1.0598 nan 0.0010 0.0002
## 420 1.0505 nan 0.0010 0.0002
## 440 1.0414 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0242 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2342 nan 0.0010 0.0003
## 120 1.2187 nan 0.0010 0.0004
## 140 1.2038 nan 0.0010 0.0003
## 160 1.1894 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
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## 220 1.1484 nan 0.0010 0.0002
## 240 1.1353 nan 0.0010 0.0003
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## 280 1.1108 nan 0.0010 0.0003
## 300 1.0992 nan 0.0010 0.0002
## 320 1.0879 nan 0.0010 0.0003
## 340 1.0768 nan 0.0010 0.0002
## 360 1.0656 nan 0.0010 0.0002
## 380 1.0549 nan 0.0010 0.0002
## 400 1.0445 nan 0.0010 0.0002
## 420 1.0347 nan 0.0010 0.0002
## 440 1.0251 nan 0.0010 0.0001
## 460 1.0157 nan 0.0010 0.0002
## 480 1.0067 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0004
## 80 1.2502 nan 0.0010 0.0004
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2036 nan 0.0010 0.0003
## 160 1.1888 nan 0.0010 0.0003
## 180 1.1748 nan 0.0010 0.0003
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## 220 1.1478 nan 0.0010 0.0003
## 240 1.1351 nan 0.0010 0.0003
## 260 1.1229 nan 0.0010 0.0002
## 280 1.1110 nan 0.0010 0.0003
## 300 1.0994 nan 0.0010 0.0002
## 320 1.0883 nan 0.0010 0.0002
## 340 1.0775 nan 0.0010 0.0002
## 360 1.0670 nan 0.0010 0.0002
## 380 1.0566 nan 0.0010 0.0002
## 400 1.0463 nan 0.0010 0.0002
## 420 1.0367 nan 0.0010 0.0002
## 440 1.0274 nan 0.0010 0.0002
## 460 1.0179 nan 0.0010 0.0002
## 480 1.0088 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0003
## 80 1.2521 nan 0.0010 0.0004
## 100 1.2363 nan 0.0010 0.0003
## 120 1.2208 nan 0.0010 0.0004
## 140 1.2060 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1779 nan 0.0010 0.0003
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## 240 1.1390 nan 0.0010 0.0003
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## 280 1.1152 nan 0.0010 0.0003
## 300 1.1038 nan 0.0010 0.0002
## 320 1.0925 nan 0.0010 0.0002
## 340 1.0816 nan 0.0010 0.0003
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## 380 1.0607 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
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## 460 1.0218 nan 0.0010 0.0002
## 480 1.0126 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0042
## 2 1.3045 nan 0.0100 0.0034
## 3 1.2965 nan 0.0100 0.0038
## 4 1.2885 nan 0.0100 0.0039
## 5 1.2806 nan 0.0100 0.0038
## 6 1.2725 nan 0.0100 0.0037
## 7 1.2649 nan 0.0100 0.0038
## 8 1.2574 nan 0.0100 0.0034
## 9 1.2498 nan 0.0100 0.0036
## 10 1.2426 nan 0.0100 0.0033
## 20 1.1760 nan 0.0100 0.0027
## 40 1.0706 nan 0.0100 0.0019
## 60 0.9896 nan 0.0100 0.0012
## 80 0.9270 nan 0.0100 0.0011
## 100 0.8769 nan 0.0100 0.0007
## 120 0.8356 nan 0.0100 0.0008
## 140 0.8029 nan 0.0100 0.0005
## 160 0.7756 nan 0.0100 0.0003
## 180 0.7504 nan 0.0100 0.0004
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## 220 0.7101 nan 0.0100 0.0002
## 240 0.6923 nan 0.0100 0.0001
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## 280 0.6620 nan 0.0100 0.0000
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## 320 0.6356 nan 0.0100 -0.0001
## 340 0.6242 nan 0.0100 0.0001
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## 380 0.6022 nan 0.0100 0.0001
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## 460 0.5629 nan 0.0100 -0.0000
## 480 0.5541 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0037
## 3 1.2957 nan 0.0100 0.0041
## 4 1.2875 nan 0.0100 0.0040
## 5 1.2789 nan 0.0100 0.0036
## 6 1.2718 nan 0.0100 0.0036
## 7 1.2637 nan 0.0100 0.0035
## 8 1.2558 nan 0.0100 0.0037
## 9 1.2487 nan 0.0100 0.0032
## 10 1.2409 nan 0.0100 0.0032
## 20 1.1747 nan 0.0100 0.0028
## 40 1.0698 nan 0.0100 0.0017
## 60 0.9923 nan 0.0100 0.0012
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## 180 0.7537 nan 0.0100 0.0002
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## 280 0.6645 nan 0.0100 -0.0000
## 300 0.6512 nan 0.0100 -0.0001
## 320 0.6392 nan 0.0100 -0.0000
## 340 0.6281 nan 0.0100 -0.0001
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## 380 0.6080 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0040
## 2 1.3053 nan 0.0100 0.0032
## 3 1.2967 nan 0.0100 0.0040
## 4 1.2885 nan 0.0100 0.0038
## 5 1.2810 nan 0.0100 0.0039
## 6 1.2746 nan 0.0100 0.0029
## 7 1.2663 nan 0.0100 0.0039
## 8 1.2591 nan 0.0100 0.0032
## 9 1.2519 nan 0.0100 0.0034
## 10 1.2451 nan 0.0100 0.0034
## 20 1.1774 nan 0.0100 0.0029
## 40 1.0736 nan 0.0100 0.0019
## 60 0.9958 nan 0.0100 0.0015
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## 180 0.7556 nan 0.0100 0.0003
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## 220 0.7164 nan 0.0100 -0.0001
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## 280 0.6703 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0043
## 2 1.3032 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2854 nan 0.0100 0.0037
## 5 1.2768 nan 0.0100 0.0039
## 6 1.2687 nan 0.0100 0.0036
## 7 1.2607 nan 0.0100 0.0036
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## 9 1.2440 nan 0.0100 0.0038
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## 20 1.1677 nan 0.0100 0.0029
## 40 1.0560 nan 0.0100 0.0021
## 60 0.9719 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0040
## 2 1.3032 nan 0.0100 0.0039
## 3 1.2943 nan 0.0100 0.0042
## 4 1.2857 nan 0.0100 0.0041
## 5 1.2782 nan 0.0100 0.0036
## 6 1.2695 nan 0.0100 0.0038
## 7 1.2618 nan 0.0100 0.0035
## 8 1.2546 nan 0.0100 0.0032
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## 40 1.0581 nan 0.0100 0.0018
## 60 0.9751 nan 0.0100 0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0041
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2849 nan 0.0100 0.0043
## 5 1.2771 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0032
## 7 1.2611 nan 0.0100 0.0037
## 8 1.2530 nan 0.0100 0.0034
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2380 nan 0.0100 0.0031
## 20 1.1684 nan 0.0100 0.0029
## 40 1.0594 nan 0.0100 0.0020
## 60 0.9764 nan 0.0100 0.0015
## 80 0.9130 nan 0.0100 0.0011
## 100 0.8613 nan 0.0100 0.0009
## 120 0.8184 nan 0.0100 0.0007
## 140 0.7828 nan 0.0100 0.0004
## 160 0.7532 nan 0.0100 0.0005
## 180 0.7259 nan 0.0100 0.0003
## 200 0.7030 nan 0.0100 0.0002
## 220 0.6827 nan 0.0100 -0.0000
## 240 0.6645 nan 0.0100 0.0003
## 260 0.6478 nan 0.0100 0.0002
## 280 0.6327 nan 0.0100 0.0002
## 300 0.6186 nan 0.0100 0.0002
## 320 0.6056 nan 0.0100 -0.0001
## 340 0.5928 nan 0.0100 0.0002
## 360 0.5804 nan 0.0100 -0.0000
## 380 0.5695 nan 0.0100 0.0001
## 400 0.5583 nan 0.0100 -0.0001
## 420 0.5481 nan 0.0100 -0.0001
## 440 0.5387 nan 0.0100 0.0001
## 460 0.5295 nan 0.0100 -0.0001
## 480 0.5199 nan 0.0100 0.0000
## 500 0.5109 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3023 nan 0.0100 0.0046
## 3 1.2929 nan 0.0100 0.0042
## 4 1.2837 nan 0.0100 0.0039
## 5 1.2750 nan 0.0100 0.0038
## 6 1.2670 nan 0.0100 0.0037
## 7 1.2587 nan 0.0100 0.0039
## 8 1.2504 nan 0.0100 0.0035
## 9 1.2423 nan 0.0100 0.0034
## 10 1.2341 nan 0.0100 0.0035
## 20 1.1606 nan 0.0100 0.0032
## 40 1.0448 nan 0.0100 0.0022
## 60 0.9571 nan 0.0100 0.0018
## 80 0.8883 nan 0.0100 0.0013
## 100 0.8307 nan 0.0100 0.0007
## 120 0.7847 nan 0.0100 0.0006
## 140 0.7456 nan 0.0100 0.0003
## 160 0.7142 nan 0.0100 0.0002
## 180 0.6857 nan 0.0100 0.0003
## 200 0.6599 nan 0.0100 0.0004
## 220 0.6358 nan 0.0100 0.0001
## 240 0.6156 nan 0.0100 -0.0001
## 260 0.5974 nan 0.0100 0.0001
## 280 0.5803 nan 0.0100 0.0002
## 300 0.5648 nan 0.0100 -0.0000
## 320 0.5506 nan 0.0100 0.0001
## 340 0.5356 nan 0.0100 0.0000
## 360 0.5230 nan 0.0100 0.0000
## 380 0.5099 nan 0.0100 -0.0001
## 400 0.4980 nan 0.0100 0.0000
## 420 0.4868 nan 0.0100 -0.0001
## 440 0.4748 nan 0.0100 0.0000
## 460 0.4642 nan 0.0100 0.0001
## 480 0.4545 nan 0.0100 -0.0001
## 500 0.4450 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3017 nan 0.0100 0.0043
## 3 1.2928 nan 0.0100 0.0040
## 4 1.2838 nan 0.0100 0.0038
## 5 1.2747 nan 0.0100 0.0040
## 6 1.2661 nan 0.0100 0.0035
## 7 1.2583 nan 0.0100 0.0033
## 8 1.2496 nan 0.0100 0.0040
## 9 1.2423 nan 0.0100 0.0029
## 10 1.2346 nan 0.0100 0.0035
## 20 1.1619 nan 0.0100 0.0032
## 40 1.0448 nan 0.0100 0.0021
## 60 0.9579 nan 0.0100 0.0015
## 80 0.8892 nan 0.0100 0.0011
## 100 0.8353 nan 0.0100 0.0010
## 120 0.7923 nan 0.0100 0.0005
## 140 0.7546 nan 0.0100 0.0003
## 160 0.7214 nan 0.0100 0.0003
## 180 0.6943 nan 0.0100 0.0002
## 200 0.6715 nan 0.0100 -0.0000
## 220 0.6487 nan 0.0100 0.0003
## 240 0.6283 nan 0.0100 0.0001
## 260 0.6099 nan 0.0100 0.0000
## 280 0.5919 nan 0.0100 0.0000
## 300 0.5761 nan 0.0100 -0.0001
## 320 0.5623 nan 0.0100 0.0001
## 340 0.5492 nan 0.0100 -0.0002
## 360 0.5367 nan 0.0100 0.0000
## 380 0.5247 nan 0.0100 0.0001
## 400 0.5126 nan 0.0100 -0.0000
## 420 0.5011 nan 0.0100 -0.0000
## 440 0.4893 nan 0.0100 0.0000
## 460 0.4784 nan 0.0100 0.0000
## 480 0.4685 nan 0.0100 -0.0000
## 500 0.4582 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0037
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2836 nan 0.0100 0.0040
## 5 1.2751 nan 0.0100 0.0040
## 6 1.2663 nan 0.0100 0.0037
## 7 1.2577 nan 0.0100 0.0038
## 8 1.2491 nan 0.0100 0.0034
## 9 1.2421 nan 0.0100 0.0035
## 10 1.2346 nan 0.0100 0.0032
## 20 1.1655 nan 0.0100 0.0031
## 40 1.0528 nan 0.0100 0.0017
## 60 0.9682 nan 0.0100 0.0015
## 80 0.9009 nan 0.0100 0.0010
## 100 0.8465 nan 0.0100 0.0009
## 120 0.8018 nan 0.0100 0.0008
## 140 0.7645 nan 0.0100 0.0007
## 160 0.7339 nan 0.0100 0.0002
## 180 0.7057 nan 0.0100 0.0003
## 200 0.6837 nan 0.0100 0.0001
## 220 0.6621 nan 0.0100 0.0002
## 240 0.6422 nan 0.0100 0.0002
## 260 0.6229 nan 0.0100 0.0001
## 280 0.6058 nan 0.0100 0.0002
## 300 0.5904 nan 0.0100 -0.0000
## 320 0.5759 nan 0.0100 0.0001
## 340 0.5625 nan 0.0100 0.0001
## 360 0.5486 nan 0.0100 0.0000
## 380 0.5373 nan 0.0100 -0.0001
## 400 0.5260 nan 0.0100 -0.0002
## 420 0.5149 nan 0.0100 -0.0000
## 440 0.5041 nan 0.0100 -0.0001
## 460 0.4937 nan 0.0100 0.0000
## 480 0.4835 nan 0.0100 -0.0001
## 500 0.4746 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0358
## 2 1.1752 nan 0.1000 0.0304
## 3 1.1165 nan 0.1000 0.0247
## 4 1.0662 nan 0.1000 0.0218
## 5 1.0242 nan 0.1000 0.0166
## 6 0.9898 nan 0.1000 0.0150
## 7 0.9526 nan 0.1000 0.0168
## 8 0.9264 nan 0.1000 0.0088
## 9 0.9043 nan 0.1000 0.0061
## 10 0.8783 nan 0.1000 0.0084
## 20 0.7208 nan 0.1000 0.0031
## 40 0.6002 nan 0.1000 -0.0002
## 60 0.5127 nan 0.1000 -0.0022
## 80 0.4498 nan 0.1000 -0.0006
## 100 0.3956 nan 0.1000 -0.0011
## 120 0.3558 nan 0.1000 -0.0003
## 140 0.3185 nan 0.1000 -0.0009
## 160 0.2824 nan 0.1000 -0.0000
## 180 0.2536 nan 0.1000 -0.0004
## 200 0.2282 nan 0.1000 -0.0003
## 220 0.2066 nan 0.1000 -0.0004
## 240 0.1874 nan 0.1000 -0.0007
## 260 0.1691 nan 0.1000 0.0002
## 280 0.1532 nan 0.1000 -0.0001
## 300 0.1377 nan 0.1000 -0.0008
## 320 0.1275 nan 0.1000 -0.0006
## 340 0.1176 nan 0.1000 -0.0004
## 360 0.1070 nan 0.1000 0.0001
## 380 0.0968 nan 0.1000 -0.0001
## 400 0.0890 nan 0.1000 -0.0001
## 420 0.0817 nan 0.1000 -0.0002
## 440 0.0763 nan 0.1000 -0.0003
## 460 0.0702 nan 0.1000 -0.0000
## 480 0.0644 nan 0.1000 -0.0001
## 500 0.0591 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0403
## 2 1.1692 nan 0.1000 0.0316
## 3 1.1088 nan 0.1000 0.0276
## 4 1.0610 nan 0.1000 0.0206
## 5 1.0175 nan 0.1000 0.0174
## 6 0.9834 nan 0.1000 0.0133
## 7 0.9494 nan 0.1000 0.0127
## 8 0.9213 nan 0.1000 0.0118
## 9 0.8969 nan 0.1000 0.0088
## 10 0.8750 nan 0.1000 0.0070
## 20 0.7253 nan 0.1000 0.0017
## 40 0.6038 nan 0.1000 -0.0022
## 60 0.5170 nan 0.1000 0.0008
## 80 0.4486 nan 0.1000 -0.0001
## 100 0.3993 nan 0.1000 -0.0011
## 120 0.3527 nan 0.1000 0.0001
## 140 0.3155 nan 0.1000 -0.0005
## 160 0.2876 nan 0.1000 -0.0007
## 180 0.2599 nan 0.1000 -0.0006
## 200 0.2331 nan 0.1000 0.0003
## 220 0.2102 nan 0.1000 -0.0008
## 240 0.1895 nan 0.1000 -0.0010
## 260 0.1719 nan 0.1000 -0.0003
## 280 0.1560 nan 0.1000 -0.0009
## 300 0.1423 nan 0.1000 -0.0006
## 320 0.1317 nan 0.1000 -0.0003
## 340 0.1209 nan 0.1000 0.0002
## 360 0.1107 nan 0.1000 -0.0004
## 380 0.1026 nan 0.1000 -0.0004
## 400 0.0950 nan 0.1000 -0.0001
## 420 0.0884 nan 0.1000 -0.0004
## 440 0.0806 nan 0.1000 -0.0002
## 460 0.0754 nan 0.1000 -0.0001
## 480 0.0686 nan 0.1000 -0.0001
## 500 0.0632 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0369
## 2 1.1697 nan 0.1000 0.0290
## 3 1.1128 nan 0.1000 0.0241
## 4 1.0638 nan 0.1000 0.0226
## 5 1.0218 nan 0.1000 0.0175
## 6 0.9861 nan 0.1000 0.0143
## 7 0.9521 nan 0.1000 0.0124
## 8 0.9258 nan 0.1000 0.0095
## 9 0.8981 nan 0.1000 0.0091
## 10 0.8749 nan 0.1000 0.0090
## 20 0.7392 nan 0.1000 0.0024
## 40 0.6119 nan 0.1000 -0.0007
## 60 0.5296 nan 0.1000 -0.0017
## 80 0.4699 nan 0.1000 -0.0001
## 100 0.4139 nan 0.1000 -0.0004
## 120 0.3705 nan 0.1000 -0.0023
## 140 0.3327 nan 0.1000 -0.0013
## 160 0.3025 nan 0.1000 -0.0002
## 180 0.2704 nan 0.1000 -0.0008
## 200 0.2490 nan 0.1000 -0.0005
## 220 0.2278 nan 0.1000 -0.0006
## 240 0.2109 nan 0.1000 -0.0008
## 260 0.1919 nan 0.1000 -0.0006
## 280 0.1748 nan 0.1000 -0.0001
## 300 0.1602 nan 0.1000 -0.0004
## 320 0.1469 nan 0.1000 -0.0006
## 340 0.1342 nan 0.1000 -0.0001
## 360 0.1241 nan 0.1000 -0.0001
## 380 0.1147 nan 0.1000 -0.0001
## 400 0.1061 nan 0.1000 -0.0004
## 420 0.0976 nan 0.1000 -0.0000
## 440 0.0903 nan 0.1000 -0.0004
## 460 0.0836 nan 0.1000 -0.0004
## 480 0.0777 nan 0.1000 -0.0005
## 500 0.0723 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2296 nan 0.1000 0.0412
## 2 1.1665 nan 0.1000 0.0269
## 3 1.1054 nan 0.1000 0.0272
## 4 1.0542 nan 0.1000 0.0220
## 5 1.0131 nan 0.1000 0.0180
## 6 0.9733 nan 0.1000 0.0165
## 7 0.9392 nan 0.1000 0.0157
## 8 0.9077 nan 0.1000 0.0120
## 9 0.8811 nan 0.1000 0.0089
## 10 0.8576 nan 0.1000 0.0058
## 20 0.7013 nan 0.1000 0.0027
## 40 0.5504 nan 0.1000 0.0009
## 60 0.4662 nan 0.1000 -0.0018
## 80 0.3911 nan 0.1000 0.0001
## 100 0.3401 nan 0.1000 -0.0016
## 120 0.2943 nan 0.1000 -0.0000
## 140 0.2581 nan 0.1000 -0.0003
## 160 0.2252 nan 0.1000 0.0004
## 180 0.1982 nan 0.1000 -0.0007
## 200 0.1765 nan 0.1000 -0.0007
## 220 0.1544 nan 0.1000 -0.0001
## 240 0.1374 nan 0.1000 -0.0001
## 260 0.1229 nan 0.1000 -0.0003
## 280 0.1072 nan 0.1000 -0.0003
## 300 0.0966 nan 0.1000 -0.0003
## 320 0.0864 nan 0.1000 -0.0002
## 340 0.0783 nan 0.1000 -0.0003
## 360 0.0705 nan 0.1000 -0.0003
## 380 0.0633 nan 0.1000 -0.0001
## 400 0.0581 nan 0.1000 -0.0002
## 420 0.0522 nan 0.1000 -0.0002
## 440 0.0468 nan 0.1000 -0.0001
## 460 0.0429 nan 0.1000 -0.0002
## 480 0.0386 nan 0.1000 -0.0001
## 500 0.0352 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0400
## 2 1.1695 nan 0.1000 0.0277
## 3 1.1041 nan 0.1000 0.0260
## 4 1.0580 nan 0.1000 0.0202
## 5 1.0139 nan 0.1000 0.0201
## 6 0.9768 nan 0.1000 0.0160
## 7 0.9415 nan 0.1000 0.0137
## 8 0.9154 nan 0.1000 0.0083
## 9 0.8874 nan 0.1000 0.0112
## 10 0.8623 nan 0.1000 0.0102
## 20 0.6988 nan 0.1000 0.0023
## 40 0.5501 nan 0.1000 0.0008
## 60 0.4619 nan 0.1000 -0.0010
## 80 0.3922 nan 0.1000 0.0003
## 100 0.3391 nan 0.1000 -0.0003
## 120 0.2990 nan 0.1000 -0.0001
## 140 0.2584 nan 0.1000 -0.0008
## 160 0.2282 nan 0.1000 -0.0006
## 180 0.2062 nan 0.1000 -0.0005
## 200 0.1815 nan 0.1000 -0.0004
## 220 0.1602 nan 0.1000 -0.0002
## 240 0.1411 nan 0.1000 -0.0009
## 260 0.1256 nan 0.1000 -0.0004
## 280 0.1133 nan 0.1000 -0.0005
## 300 0.1013 nan 0.1000 -0.0004
## 320 0.0901 nan 0.1000 -0.0005
## 340 0.0807 nan 0.1000 -0.0004
## 360 0.0715 nan 0.1000 -0.0001
## 380 0.0644 nan 0.1000 -0.0001
## 400 0.0585 nan 0.1000 -0.0003
## 420 0.0521 nan 0.1000 -0.0002
## 440 0.0476 nan 0.1000 -0.0003
## 460 0.0432 nan 0.1000 -0.0001
## 480 0.0389 nan 0.1000 -0.0002
## 500 0.0358 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0347
## 2 1.1683 nan 0.1000 0.0303
## 3 1.1046 nan 0.1000 0.0252
## 4 1.0551 nan 0.1000 0.0200
## 5 1.0058 nan 0.1000 0.0218
## 6 0.9703 nan 0.1000 0.0149
## 7 0.9344 nan 0.1000 0.0126
## 8 0.9039 nan 0.1000 0.0122
## 9 0.8747 nan 0.1000 0.0106
## 10 0.8524 nan 0.1000 0.0089
## 20 0.6977 nan 0.1000 0.0040
## 40 0.5655 nan 0.1000 -0.0010
## 60 0.4718 nan 0.1000 0.0001
## 80 0.4061 nan 0.1000 -0.0006
## 100 0.3485 nan 0.1000 -0.0006
## 120 0.3049 nan 0.1000 -0.0015
## 140 0.2671 nan 0.1000 -0.0009
## 160 0.2351 nan 0.1000 0.0001
## 180 0.2078 nan 0.1000 -0.0007
## 200 0.1830 nan 0.1000 -0.0005
## 220 0.1620 nan 0.1000 -0.0003
## 240 0.1428 nan 0.1000 -0.0005
## 260 0.1277 nan 0.1000 -0.0003
## 280 0.1141 nan 0.1000 -0.0002
## 300 0.1031 nan 0.1000 -0.0003
## 320 0.0923 nan 0.1000 -0.0003
## 340 0.0833 nan 0.1000 -0.0004
## 360 0.0752 nan 0.1000 -0.0003
## 380 0.0678 nan 0.1000 -0.0002
## 400 0.0618 nan 0.1000 -0.0002
## 420 0.0562 nan 0.1000 -0.0002
## 440 0.0506 nan 0.1000 -0.0002
## 460 0.0458 nan 0.1000 -0.0003
## 480 0.0416 nan 0.1000 -0.0002
## 500 0.0379 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0387
## 2 1.1539 nan 0.1000 0.0296
## 3 1.0902 nan 0.1000 0.0275
## 4 1.0385 nan 0.1000 0.0241
## 5 0.9909 nan 0.1000 0.0188
## 6 0.9499 nan 0.1000 0.0175
## 7 0.9138 nan 0.1000 0.0134
## 8 0.8834 nan 0.1000 0.0112
## 9 0.8534 nan 0.1000 0.0079
## 10 0.8249 nan 0.1000 0.0115
## 20 0.6683 nan 0.1000 -0.0009
## 40 0.5142 nan 0.1000 -0.0010
## 60 0.4132 nan 0.1000 0.0010
## 80 0.3404 nan 0.1000 -0.0004
## 100 0.2777 nan 0.1000 0.0003
## 120 0.2316 nan 0.1000 -0.0009
## 140 0.1921 nan 0.1000 -0.0001
## 160 0.1633 nan 0.1000 -0.0004
## 180 0.1407 nan 0.1000 -0.0002
## 200 0.1208 nan 0.1000 -0.0002
## 220 0.1051 nan 0.1000 -0.0002
## 240 0.0925 nan 0.1000 -0.0002
## 260 0.0811 nan 0.1000 -0.0002
## 280 0.0698 nan 0.1000 -0.0001
## 300 0.0614 nan 0.1000 -0.0000
## 320 0.0545 nan 0.1000 -0.0002
## 340 0.0479 nan 0.1000 0.0000
## 360 0.0425 nan 0.1000 -0.0001
## 380 0.0377 nan 0.1000 -0.0001
## 400 0.0331 nan 0.1000 -0.0001
## 420 0.0296 nan 0.1000 -0.0001
## 440 0.0263 nan 0.1000 -0.0000
## 460 0.0231 nan 0.1000 -0.0000
## 480 0.0206 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0397
## 2 1.1515 nan 0.1000 0.0355
## 3 1.0871 nan 0.1000 0.0321
## 4 1.0350 nan 0.1000 0.0234
## 5 0.9874 nan 0.1000 0.0200
## 6 0.9496 nan 0.1000 0.0161
## 7 0.9155 nan 0.1000 0.0123
## 8 0.8798 nan 0.1000 0.0133
## 9 0.8503 nan 0.1000 0.0102
## 10 0.8247 nan 0.1000 0.0076
## 20 0.6626 nan 0.1000 0.0014
## 40 0.5103 nan 0.1000 0.0002
## 60 0.4190 nan 0.1000 -0.0011
## 80 0.3520 nan 0.1000 -0.0001
## 100 0.2921 nan 0.1000 0.0003
## 120 0.2461 nan 0.1000 -0.0006
## 140 0.2080 nan 0.1000 -0.0011
## 160 0.1774 nan 0.1000 -0.0005
## 180 0.1512 nan 0.1000 -0.0001
## 200 0.1289 nan 0.1000 -0.0005
## 220 0.1112 nan 0.1000 -0.0004
## 240 0.0973 nan 0.1000 -0.0006
## 260 0.0846 nan 0.1000 -0.0001
## 280 0.0743 nan 0.1000 -0.0003
## 300 0.0649 nan 0.1000 -0.0004
## 320 0.0568 nan 0.1000 -0.0001
## 340 0.0498 nan 0.1000 0.0000
## 360 0.0442 nan 0.1000 -0.0000
## 380 0.0392 nan 0.1000 -0.0001
## 400 0.0348 nan 0.1000 -0.0001
## 420 0.0305 nan 0.1000 -0.0001
## 440 0.0269 nan 0.1000 -0.0001
## 460 0.0236 nan 0.1000 -0.0001
## 480 0.0208 nan 0.1000 -0.0000
## 500 0.0184 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2325 nan 0.1000 0.0381
## 2 1.1601 nan 0.1000 0.0322
## 3 1.0948 nan 0.1000 0.0288
## 4 1.0455 nan 0.1000 0.0211
## 5 1.0002 nan 0.1000 0.0195
## 6 0.9634 nan 0.1000 0.0144
## 7 0.9291 nan 0.1000 0.0121
## 8 0.8974 nan 0.1000 0.0118
## 9 0.8719 nan 0.1000 0.0094
## 10 0.8458 nan 0.1000 0.0108
## 20 0.6820 nan 0.1000 0.0032
## 40 0.5295 nan 0.1000 0.0008
## 60 0.4409 nan 0.1000 -0.0015
## 80 0.3730 nan 0.1000 -0.0010
## 100 0.3152 nan 0.1000 -0.0013
## 120 0.2679 nan 0.1000 0.0002
## 140 0.2293 nan 0.1000 -0.0008
## 160 0.1970 nan 0.1000 -0.0010
## 180 0.1722 nan 0.1000 -0.0003
## 200 0.1504 nan 0.1000 -0.0009
## 220 0.1310 nan 0.1000 -0.0007
## 240 0.1151 nan 0.1000 -0.0006
## 260 0.1011 nan 0.1000 -0.0004
## 280 0.0884 nan 0.1000 -0.0004
## 300 0.0775 nan 0.1000 -0.0005
## 320 0.0687 nan 0.1000 -0.0001
## 340 0.0617 nan 0.1000 -0.0002
## 360 0.0545 nan 0.1000 -0.0002
## 380 0.0474 nan 0.1000 -0.0002
## 400 0.0423 nan 0.1000 -0.0002
## 420 0.0374 nan 0.1000 -0.0002
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## 460 0.0294 nan 0.1000 -0.0002
## 480 0.0260 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0005
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0004
## 40 1.2863 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2534 nan 0.0010 0.0003
## 100 1.2383 nan 0.0010 0.0004
## 120 1.2233 nan 0.0010 0.0003
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
## 200 1.1676 nan 0.0010 0.0003
## 220 1.1548 nan 0.0010 0.0003
## 240 1.1424 nan 0.0010 0.0003
## 260 1.1305 nan 0.0010 0.0002
## 280 1.1187 nan 0.0010 0.0003
## 300 1.1072 nan 0.0010 0.0003
## 320 1.0964 nan 0.0010 0.0002
## 340 1.0856 nan 0.0010 0.0002
## 360 1.0751 nan 0.0010 0.0002
## 380 1.0646 nan 0.0010 0.0002
## 400 1.0544 nan 0.0010 0.0002
## 420 1.0446 nan 0.0010 0.0002
## 440 1.0351 nan 0.0010 0.0002
## 460 1.0260 nan 0.0010 0.0002
## 480 1.0172 nan 0.0010 0.0002
## 500 1.0083 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0003
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0004
## 120 1.2235 nan 0.0010 0.0003
## 140 1.2090 nan 0.0010 0.0003
## 160 1.1949 nan 0.0010 0.0003
## 180 1.1815 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1556 nan 0.0010 0.0002
## 240 1.1435 nan 0.0010 0.0003
## 260 1.1313 nan 0.0010 0.0003
## 280 1.1196 nan 0.0010 0.0003
## 300 1.1082 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0861 nan 0.0010 0.0002
## 360 1.0758 nan 0.0010 0.0002
## 380 1.0654 nan 0.0010 0.0002
## 400 1.0556 nan 0.0010 0.0002
## 420 1.0460 nan 0.0010 0.0002
## 440 1.0364 nan 0.0010 0.0002
## 460 1.0273 nan 0.0010 0.0002
## 480 1.0183 nan 0.0010 0.0002
## 500 1.0095 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0003
## 9 1.3132 nan 0.0010 0.0003
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2542 nan 0.0010 0.0004
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0004
## 160 1.1962 nan 0.0010 0.0003
## 180 1.1826 nan 0.0010 0.0003
## 200 1.1695 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
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## 280 1.1208 nan 0.0010 0.0003
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## 320 1.0987 nan 0.0010 0.0002
## 340 1.0879 nan 0.0010 0.0002
## 360 1.0777 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0575 nan 0.0010 0.0002
## 420 1.0476 nan 0.0010 0.0002
## 440 1.0384 nan 0.0010 0.0001
## 460 1.0292 nan 0.0010 0.0002
## 480 1.0205 nan 0.0010 0.0002
## 500 1.0119 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0005
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2495 nan 0.0010 0.0003
## 100 1.2327 nan 0.0010 0.0004
## 120 1.2169 nan 0.0010 0.0004
## 140 1.2012 nan 0.0010 0.0004
## 160 1.1862 nan 0.0010 0.0004
## 180 1.1718 nan 0.0010 0.0003
## 200 1.1580 nan 0.0010 0.0003
## 220 1.1443 nan 0.0010 0.0003
## 240 1.1313 nan 0.0010 0.0002
## 260 1.1186 nan 0.0010 0.0003
## 280 1.1061 nan 0.0010 0.0003
## 300 1.0941 nan 0.0010 0.0002
## 320 1.0824 nan 0.0010 0.0003
## 340 1.0708 nan 0.0010 0.0003
## 360 1.0598 nan 0.0010 0.0002
## 380 1.0491 nan 0.0010 0.0002
## 400 1.0387 nan 0.0010 0.0002
## 420 1.0285 nan 0.0010 0.0002
## 440 1.0187 nan 0.0010 0.0002
## 460 1.0090 nan 0.0010 0.0002
## 480 0.9996 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2341 nan 0.0010 0.0004
## 120 1.2186 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0003
## 160 1.1884 nan 0.0010 0.0004
## 180 1.1741 nan 0.0010 0.0003
## 200 1.1601 nan 0.0010 0.0003
## 220 1.1468 nan 0.0010 0.0003
## 240 1.1336 nan 0.0010 0.0003
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## 280 1.1084 nan 0.0010 0.0003
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## 320 1.0848 nan 0.0010 0.0003
## 340 1.0734 nan 0.0010 0.0002
## 360 1.0624 nan 0.0010 0.0002
## 380 1.0518 nan 0.0010 0.0002
## 400 1.0411 nan 0.0010 0.0002
## 420 1.0310 nan 0.0010 0.0002
## 440 1.0211 nan 0.0010 0.0002
## 460 1.0113 nan 0.0010 0.0002
## 480 1.0020 nan 0.0010 0.0002
## 500 0.9930 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0005
## 3 1.3183 nan 0.0010 0.0005
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2343 nan 0.0010 0.0004
## 120 1.2188 nan 0.0010 0.0003
## 140 1.2041 nan 0.0010 0.0003
## 160 1.1894 nan 0.0010 0.0003
## 180 1.1751 nan 0.0010 0.0003
## 200 1.1610 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1347 nan 0.0010 0.0003
## 260 1.1221 nan 0.0010 0.0003
## 280 1.1098 nan 0.0010 0.0003
## 300 1.0978 nan 0.0010 0.0003
## 320 1.0861 nan 0.0010 0.0003
## 340 1.0749 nan 0.0010 0.0002
## 360 1.0639 nan 0.0010 0.0002
## 380 1.0535 nan 0.0010 0.0002
## 400 1.0433 nan 0.0010 0.0002
## 420 1.0333 nan 0.0010 0.0002
## 440 1.0237 nan 0.0010 0.0002
## 460 1.0143 nan 0.0010 0.0002
## 480 1.0051 nan 0.0010 0.0002
## 500 0.9961 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3181 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0005
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0005
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3010 nan 0.0010 0.0004
## 40 1.2818 nan 0.0010 0.0004
## 60 1.2635 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0004
## 100 1.2290 nan 0.0010 0.0003
## 120 1.2127 nan 0.0010 0.0004
## 140 1.1967 nan 0.0010 0.0004
## 160 1.1811 nan 0.0010 0.0004
## 180 1.1660 nan 0.0010 0.0003
## 200 1.1511 nan 0.0010 0.0003
## 220 1.1367 nan 0.0010 0.0003
## 240 1.1231 nan 0.0010 0.0003
## 260 1.1098 nan 0.0010 0.0002
## 280 1.0967 nan 0.0010 0.0003
## 300 1.0841 nan 0.0010 0.0003
## 320 1.0721 nan 0.0010 0.0003
## 340 1.0603 nan 0.0010 0.0002
## 360 1.0488 nan 0.0010 0.0003
## 380 1.0375 nan 0.0010 0.0003
## 400 1.0267 nan 0.0010 0.0002
## 420 1.0158 nan 0.0010 0.0003
## 440 1.0056 nan 0.0010 0.0002
## 460 0.9955 nan 0.0010 0.0002
## 480 0.9856 nan 0.0010 0.0002
## 500 0.9760 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2471 nan 0.0010 0.0004
## 100 1.2303 nan 0.0010 0.0004
## 120 1.2137 nan 0.0010 0.0004
## 140 1.1979 nan 0.0010 0.0004
## 160 1.1823 nan 0.0010 0.0004
## 180 1.1673 nan 0.0010 0.0003
## 200 1.1528 nan 0.0010 0.0003
## 220 1.1387 nan 0.0010 0.0004
## 240 1.1250 nan 0.0010 0.0003
## 260 1.1120 nan 0.0010 0.0003
## 280 1.0993 nan 0.0010 0.0003
## 300 1.0869 nan 0.0010 0.0002
## 320 1.0751 nan 0.0010 0.0003
## 340 1.0631 nan 0.0010 0.0002
## 360 1.0517 nan 0.0010 0.0002
## 380 1.0406 nan 0.0010 0.0002
## 400 1.0299 nan 0.0010 0.0002
## 420 1.0194 nan 0.0010 0.0002
## 440 1.0092 nan 0.0010 0.0002
## 460 0.9992 nan 0.0010 0.0002
## 480 0.9896 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3201 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0005
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0005
## 40 1.2837 nan 0.0010 0.0004
## 60 1.2658 nan 0.0010 0.0004
## 80 1.2486 nan 0.0010 0.0004
## 100 1.2317 nan 0.0010 0.0004
## 120 1.2157 nan 0.0010 0.0004
## 140 1.2000 nan 0.0010 0.0003
## 160 1.1847 nan 0.0010 0.0004
## 180 1.1700 nan 0.0010 0.0003
## 200 1.1556 nan 0.0010 0.0003
## 220 1.1418 nan 0.0010 0.0003
## 240 1.1285 nan 0.0010 0.0003
## 260 1.1152 nan 0.0010 0.0003
## 280 1.1026 nan 0.0010 0.0003
## 300 1.0902 nan 0.0010 0.0003
## 320 1.0783 nan 0.0010 0.0003
## 340 1.0665 nan 0.0010 0.0002
## 360 1.0553 nan 0.0010 0.0003
## 380 1.0443 nan 0.0010 0.0002
## 400 1.0337 nan 0.0010 0.0002
## 420 1.0234 nan 0.0010 0.0002
## 440 1.0135 nan 0.0010 0.0002
## 460 1.0035 nan 0.0010 0.0002
## 480 0.9941 nan 0.0010 0.0002
## 500 0.9847 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0043
## 2 1.3031 nan 0.0100 0.0039
## 3 1.2952 nan 0.0100 0.0037
## 4 1.2866 nan 0.0100 0.0038
## 5 1.2784 nan 0.0100 0.0040
## 6 1.2707 nan 0.0100 0.0036
## 7 1.2635 nan 0.0100 0.0034
## 8 1.2550 nan 0.0100 0.0037
## 9 1.2473 nan 0.0100 0.0035
## 10 1.2397 nan 0.0100 0.0039
## 20 1.1680 nan 0.0100 0.0030
## 40 1.0536 nan 0.0100 0.0024
## 60 0.9652 nan 0.0100 0.0015
## 80 0.8977 nan 0.0100 0.0014
## 100 0.8442 nan 0.0100 0.0009
## 120 0.8001 nan 0.0100 0.0010
## 140 0.7647 nan 0.0100 0.0005
## 160 0.7339 nan 0.0100 0.0006
## 180 0.7086 nan 0.0100 0.0003
## 200 0.6862 nan 0.0100 0.0003
## 220 0.6659 nan 0.0100 0.0001
## 240 0.6497 nan 0.0100 0.0002
## 260 0.6337 nan 0.0100 0.0001
## 280 0.6189 nan 0.0100 -0.0001
## 300 0.6046 nan 0.0100 0.0000
## 320 0.5919 nan 0.0100 0.0000
## 340 0.5799 nan 0.0100 -0.0001
## 360 0.5694 nan 0.0100 -0.0000
## 380 0.5599 nan 0.0100 -0.0002
## 400 0.5497 nan 0.0100 0.0001
## 420 0.5415 nan 0.0100 0.0001
## 440 0.5319 nan 0.0100 -0.0000
## 460 0.5241 nan 0.0100 0.0001
## 480 0.5155 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0041
## 2 1.3032 nan 0.0100 0.0040
## 3 1.2944 nan 0.0100 0.0040
## 4 1.2857 nan 0.0100 0.0039
## 5 1.2776 nan 0.0100 0.0038
## 6 1.2692 nan 0.0100 0.0036
## 7 1.2612 nan 0.0100 0.0038
## 8 1.2531 nan 0.0100 0.0041
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2374 nan 0.0100 0.0033
## 20 1.1666 nan 0.0100 0.0028
## 40 1.0544 nan 0.0100 0.0021
## 60 0.9686 nan 0.0100 0.0017
## 80 0.9005 nan 0.0100 0.0014
## 100 0.8476 nan 0.0100 0.0010
## 120 0.8027 nan 0.0100 0.0005
## 140 0.7668 nan 0.0100 0.0006
## 160 0.7361 nan 0.0100 0.0005
## 180 0.7109 nan 0.0100 0.0004
## 200 0.6885 nan 0.0100 0.0001
## 220 0.6699 nan 0.0100 0.0001
## 240 0.6525 nan 0.0100 0.0003
## 260 0.6363 nan 0.0100 0.0000
## 280 0.6222 nan 0.0100 0.0000
## 300 0.6088 nan 0.0100 0.0001
## 320 0.5973 nan 0.0100 -0.0000
## 340 0.5864 nan 0.0100 0.0001
## 360 0.5763 nan 0.0100 0.0000
## 380 0.5670 nan 0.0100 -0.0001
## 400 0.5573 nan 0.0100 -0.0000
## 420 0.5484 nan 0.0100 -0.0000
## 440 0.5397 nan 0.0100 -0.0002
## 460 0.5314 nan 0.0100 -0.0000
## 480 0.5229 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0035
## 2 1.3049 nan 0.0100 0.0036
## 3 1.2961 nan 0.0100 0.0041
## 4 1.2872 nan 0.0100 0.0038
## 5 1.2792 nan 0.0100 0.0037
## 6 1.2704 nan 0.0100 0.0041
## 7 1.2621 nan 0.0100 0.0036
## 8 1.2547 nan 0.0100 0.0034
## 9 1.2472 nan 0.0100 0.0037
## 10 1.2394 nan 0.0100 0.0037
## 20 1.1692 nan 0.0100 0.0026
## 40 1.0600 nan 0.0100 0.0019
## 60 0.9721 nan 0.0100 0.0016
## 80 0.9044 nan 0.0100 0.0010
## 100 0.8512 nan 0.0100 0.0011
## 120 0.8074 nan 0.0100 0.0007
## 140 0.7717 nan 0.0100 0.0003
## 160 0.7415 nan 0.0100 0.0003
## 180 0.7150 nan 0.0100 0.0004
## 200 0.6944 nan 0.0100 0.0002
## 220 0.6748 nan 0.0100 0.0001
## 240 0.6575 nan 0.0100 0.0002
## 260 0.6429 nan 0.0100 0.0000
## 280 0.6295 nan 0.0100 -0.0001
## 300 0.6165 nan 0.0100 -0.0001
## 320 0.6045 nan 0.0100 0.0000
## 340 0.5933 nan 0.0100 0.0001
## 360 0.5833 nan 0.0100 -0.0000
## 380 0.5744 nan 0.0100 0.0001
## 400 0.5641 nan 0.0100 -0.0001
## 420 0.5555 nan 0.0100 -0.0001
## 440 0.5472 nan 0.0100 -0.0001
## 460 0.5395 nan 0.0100 -0.0001
## 480 0.5310 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0045
## 2 1.3024 nan 0.0100 0.0047
## 3 1.2935 nan 0.0100 0.0039
## 4 1.2840 nan 0.0100 0.0041
## 5 1.2755 nan 0.0100 0.0042
## 6 1.2665 nan 0.0100 0.0037
## 7 1.2584 nan 0.0100 0.0033
## 8 1.2498 nan 0.0100 0.0035
## 9 1.2414 nan 0.0100 0.0034
## 10 1.2333 nan 0.0100 0.0036
## 20 1.1574 nan 0.0100 0.0034
## 40 1.0370 nan 0.0100 0.0024
## 60 0.9470 nan 0.0100 0.0016
## 80 0.8781 nan 0.0100 0.0011
## 100 0.8198 nan 0.0100 0.0011
## 120 0.7755 nan 0.0100 0.0008
## 140 0.7382 nan 0.0100 0.0004
## 160 0.7072 nan 0.0100 0.0004
## 180 0.6796 nan 0.0100 0.0005
## 200 0.6553 nan 0.0100 0.0003
## 220 0.6339 nan 0.0100 0.0001
## 240 0.6155 nan 0.0100 0.0003
## 260 0.5994 nan 0.0100 -0.0002
## 280 0.5827 nan 0.0100 0.0001
## 300 0.5687 nan 0.0100 0.0000
## 320 0.5557 nan 0.0100 -0.0001
## 340 0.5424 nan 0.0100 -0.0000
## 360 0.5304 nan 0.0100 0.0000
## 380 0.5185 nan 0.0100 0.0000
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## 460 0.4776 nan 0.0100 0.0000
## 480 0.4688 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0046
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2923 nan 0.0100 0.0040
## 4 1.2834 nan 0.0100 0.0045
## 5 1.2746 nan 0.0100 0.0038
## 6 1.2653 nan 0.0100 0.0042
## 7 1.2572 nan 0.0100 0.0038
## 8 1.2493 nan 0.0100 0.0037
## 9 1.2412 nan 0.0100 0.0038
## 10 1.2332 nan 0.0100 0.0035
## 20 1.1595 nan 0.0100 0.0028
## 40 1.0404 nan 0.0100 0.0025
## 60 0.9512 nan 0.0100 0.0019
## 80 0.8797 nan 0.0100 0.0013
## 100 0.8232 nan 0.0100 0.0007
## 120 0.7777 nan 0.0100 0.0010
## 140 0.7395 nan 0.0100 0.0006
## 160 0.7076 nan 0.0100 0.0003
## 180 0.6809 nan 0.0100 0.0003
## 200 0.6563 nan 0.0100 0.0001
## 220 0.6353 nan 0.0100 0.0002
## 240 0.6164 nan 0.0100 0.0000
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## 320 0.5586 nan 0.0100 -0.0001
## 340 0.5469 nan 0.0100 0.0001
## 360 0.5352 nan 0.0100 0.0000
## 380 0.5238 nan 0.0100 -0.0001
## 400 0.5135 nan 0.0100 -0.0000
## 420 0.5037 nan 0.0100 -0.0001
## 440 0.4943 nan 0.0100 -0.0002
## 460 0.4851 nan 0.0100 -0.0002
## 480 0.4761 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3028 nan 0.0100 0.0034
## 3 1.2935 nan 0.0100 0.0045
## 4 1.2844 nan 0.0100 0.0041
## 5 1.2764 nan 0.0100 0.0037
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2590 nan 0.0100 0.0040
## 8 1.2509 nan 0.0100 0.0036
## 9 1.2431 nan 0.0100 0.0033
## 10 1.2351 nan 0.0100 0.0034
## 20 1.1635 nan 0.0100 0.0031
## 40 1.0441 nan 0.0100 0.0020
## 60 0.9556 nan 0.0100 0.0015
## 80 0.8853 nan 0.0100 0.0013
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## 120 0.7840 nan 0.0100 0.0008
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## 160 0.7151 nan 0.0100 0.0004
## 180 0.6870 nan 0.0100 0.0002
## 200 0.6641 nan 0.0100 0.0003
## 220 0.6439 nan 0.0100 -0.0002
## 240 0.6270 nan 0.0100 0.0002
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## 320 0.5701 nan 0.0100 0.0001
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## 380 0.5358 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0046
## 2 1.3013 nan 0.0100 0.0047
## 3 1.2914 nan 0.0100 0.0044
## 4 1.2818 nan 0.0100 0.0043
## 5 1.2735 nan 0.0100 0.0041
## 6 1.2653 nan 0.0100 0.0037
## 7 1.2566 nan 0.0100 0.0038
## 8 1.2472 nan 0.0100 0.0043
## 9 1.2393 nan 0.0100 0.0032
## 10 1.2310 nan 0.0100 0.0034
## 20 1.1546 nan 0.0100 0.0032
## 40 1.0318 nan 0.0100 0.0026
## 60 0.9385 nan 0.0100 0.0017
## 80 0.8650 nan 0.0100 0.0012
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## 120 0.7556 nan 0.0100 0.0009
## 140 0.7144 nan 0.0100 0.0004
## 160 0.6811 nan 0.0100 0.0005
## 180 0.6536 nan 0.0100 0.0002
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## 300 0.5337 nan 0.0100 -0.0002
## 320 0.5199 nan 0.0100 0.0001
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## 460 0.4361 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0050
## 2 1.3010 nan 0.0100 0.0047
## 3 1.2907 nan 0.0100 0.0046
## 4 1.2815 nan 0.0100 0.0042
## 5 1.2725 nan 0.0100 0.0041
## 6 1.2632 nan 0.0100 0.0039
## 7 1.2540 nan 0.0100 0.0039
## 8 1.2451 nan 0.0100 0.0045
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## 10 1.2286 nan 0.0100 0.0040
## 20 1.1528 nan 0.0100 0.0032
## 40 1.0306 nan 0.0100 0.0025
## 60 0.9357 nan 0.0100 0.0019
## 80 0.8622 nan 0.0100 0.0013
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## 120 0.7550 nan 0.0100 0.0007
## 140 0.7167 nan 0.0100 0.0005
## 160 0.6832 nan 0.0100 0.0003
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## 220 0.6083 nan 0.0100 0.0001
## 240 0.5886 nan 0.0100 0.0001
## 260 0.5708 nan 0.0100 0.0002
## 280 0.5545 nan 0.0100 0.0001
## 300 0.5401 nan 0.0100 0.0001
## 320 0.5260 nan 0.0100 0.0000
## 340 0.5118 nan 0.0100 -0.0001
## 360 0.4993 nan 0.0100 -0.0001
## 380 0.4880 nan 0.0100 -0.0000
## 400 0.4773 nan 0.0100 0.0000
## 420 0.4669 nan 0.0100 0.0001
## 440 0.4565 nan 0.0100 -0.0000
## 460 0.4462 nan 0.0100 -0.0001
## 480 0.4359 nan 0.0100 -0.0001
## 500 0.4264 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0044
## 2 1.3020 nan 0.0100 0.0041
## 3 1.2927 nan 0.0100 0.0045
## 4 1.2834 nan 0.0100 0.0043
## 5 1.2748 nan 0.0100 0.0042
## 6 1.2652 nan 0.0100 0.0044
## 7 1.2564 nan 0.0100 0.0040
## 8 1.2463 nan 0.0100 0.0043
## 9 1.2377 nan 0.0100 0.0035
## 10 1.2293 nan 0.0100 0.0039
## 20 1.1521 nan 0.0100 0.0030
## 40 1.0325 nan 0.0100 0.0022
## 60 0.9403 nan 0.0100 0.0017
## 80 0.8680 nan 0.0100 0.0011
## 100 0.8095 nan 0.0100 0.0008
## 120 0.7616 nan 0.0100 0.0007
## 140 0.7225 nan 0.0100 0.0005
## 160 0.6905 nan 0.0100 0.0003
## 180 0.6619 nan 0.0100 0.0003
## 200 0.6384 nan 0.0100 0.0002
## 220 0.6172 nan 0.0100 0.0004
## 240 0.5964 nan 0.0100 0.0003
## 260 0.5802 nan 0.0100 0.0000
## 280 0.5644 nan 0.0100 -0.0001
## 300 0.5495 nan 0.0100 -0.0000
## 320 0.5350 nan 0.0100 -0.0001
## 340 0.5225 nan 0.0100 -0.0001
## 360 0.5103 nan 0.0100 -0.0002
## 380 0.4986 nan 0.0100 -0.0002
## 400 0.4871 nan 0.0100 0.0000
## 420 0.4776 nan 0.0100 -0.0000
## 440 0.4672 nan 0.0100 -0.0000
## 460 0.4580 nan 0.0100 -0.0001
## 480 0.4485 nan 0.0100 -0.0001
## 500 0.4387 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2409 nan 0.1000 0.0376
## 2 1.1674 nan 0.1000 0.0334
## 3 1.1042 nan 0.1000 0.0302
## 4 1.0469 nan 0.1000 0.0250
## 5 1.0013 nan 0.1000 0.0187
## 6 0.9596 nan 0.1000 0.0181
## 7 0.9234 nan 0.1000 0.0141
## 8 0.8953 nan 0.1000 0.0105
## 9 0.8705 nan 0.1000 0.0121
## 10 0.8460 nan 0.1000 0.0080
## 20 0.6871 nan 0.1000 0.0038
## 40 0.5550 nan 0.1000 -0.0007
## 60 0.4788 nan 0.1000 0.0002
## 80 0.4161 nan 0.1000 -0.0001
## 100 0.3672 nan 0.1000 -0.0007
## 120 0.3215 nan 0.1000 -0.0007
## 140 0.2874 nan 0.1000 -0.0010
## 160 0.2593 nan 0.1000 -0.0006
## 180 0.2331 nan 0.1000 -0.0003
## 200 0.2091 nan 0.1000 -0.0000
## 220 0.1900 nan 0.1000 -0.0005
## 240 0.1725 nan 0.1000 -0.0005
## 260 0.1583 nan 0.1000 -0.0003
## 280 0.1450 nan 0.1000 -0.0002
## 300 0.1327 nan 0.1000 -0.0004
## 320 0.1221 nan 0.1000 -0.0004
## 340 0.1125 nan 0.1000 -0.0006
## 360 0.1022 nan 0.1000 0.0000
## 380 0.0945 nan 0.1000 -0.0001
## 400 0.0878 nan 0.1000 -0.0002
## 420 0.0804 nan 0.1000 -0.0005
## 440 0.0743 nan 0.1000 -0.0001
## 460 0.0688 nan 0.1000 -0.0002
## 480 0.0636 nan 0.1000 -0.0000
## 500 0.0589 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0363
## 2 1.1628 nan 0.1000 0.0322
## 3 1.0994 nan 0.1000 0.0310
## 4 1.0475 nan 0.1000 0.0201
## 5 1.0022 nan 0.1000 0.0189
## 6 0.9618 nan 0.1000 0.0183
## 7 0.9246 nan 0.1000 0.0160
## 8 0.8950 nan 0.1000 0.0133
## 9 0.8704 nan 0.1000 0.0110
## 10 0.8399 nan 0.1000 0.0131
## 20 0.6891 nan 0.1000 0.0020
## 40 0.5571 nan 0.1000 -0.0003
## 60 0.4825 nan 0.1000 -0.0010
## 80 0.4206 nan 0.1000 -0.0007
## 100 0.3699 nan 0.1000 -0.0004
## 120 0.3311 nan 0.1000 0.0001
## 140 0.2961 nan 0.1000 -0.0012
## 160 0.2670 nan 0.1000 -0.0008
## 180 0.2400 nan 0.1000 -0.0010
## 200 0.2150 nan 0.1000 -0.0007
## 220 0.1956 nan 0.1000 -0.0006
## 240 0.1806 nan 0.1000 -0.0010
## 260 0.1633 nan 0.1000 -0.0009
## 280 0.1507 nan 0.1000 -0.0006
## 300 0.1371 nan 0.1000 -0.0004
## 320 0.1256 nan 0.1000 -0.0005
## 340 0.1162 nan 0.1000 -0.0002
## 360 0.1067 nan 0.1000 -0.0002
## 380 0.0981 nan 0.1000 -0.0003
## 400 0.0899 nan 0.1000 -0.0001
## 420 0.0822 nan 0.1000 -0.0003
## 440 0.0763 nan 0.1000 -0.0003
## 460 0.0695 nan 0.1000 -0.0001
## 480 0.0641 nan 0.1000 -0.0003
## 500 0.0595 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0335
## 2 1.1654 nan 0.1000 0.0316
## 3 1.0993 nan 0.1000 0.0298
## 4 1.0537 nan 0.1000 0.0194
## 5 1.0079 nan 0.1000 0.0194
## 6 0.9666 nan 0.1000 0.0160
## 7 0.9312 nan 0.1000 0.0144
## 8 0.8988 nan 0.1000 0.0142
## 9 0.8726 nan 0.1000 0.0105
## 10 0.8498 nan 0.1000 0.0062
## 20 0.7075 nan 0.1000 0.0018
## 40 0.5776 nan 0.1000 0.0005
## 60 0.4946 nan 0.1000 -0.0011
## 80 0.4385 nan 0.1000 -0.0003
## 100 0.3841 nan 0.1000 0.0000
## 120 0.3490 nan 0.1000 -0.0009
## 140 0.3140 nan 0.1000 -0.0011
## 160 0.2843 nan 0.1000 -0.0018
## 180 0.2563 nan 0.1000 -0.0006
## 200 0.2319 nan 0.1000 -0.0006
## 220 0.2137 nan 0.1000 -0.0010
## 240 0.1953 nan 0.1000 -0.0004
## 260 0.1803 nan 0.1000 -0.0008
## 280 0.1645 nan 0.1000 -0.0001
## 300 0.1513 nan 0.1000 -0.0006
## 320 0.1396 nan 0.1000 -0.0005
## 340 0.1287 nan 0.1000 -0.0009
## 360 0.1192 nan 0.1000 -0.0005
## 380 0.1101 nan 0.1000 -0.0006
## 400 0.1027 nan 0.1000 -0.0001
## 420 0.0944 nan 0.1000 -0.0004
## 440 0.0867 nan 0.1000 -0.0003
## 460 0.0808 nan 0.1000 -0.0003
## 480 0.0753 nan 0.1000 -0.0003
## 500 0.0703 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2298 nan 0.1000 0.0408
## 2 1.1522 nan 0.1000 0.0339
## 3 1.0882 nan 0.1000 0.0291
## 4 1.0342 nan 0.1000 0.0221
## 5 0.9854 nan 0.1000 0.0226
## 6 0.9468 nan 0.1000 0.0161
## 7 0.9061 nan 0.1000 0.0171
## 8 0.8728 nan 0.1000 0.0141
## 9 0.8452 nan 0.1000 0.0115
## 10 0.8197 nan 0.1000 0.0089
## 20 0.6470 nan 0.1000 0.0015
## 40 0.5027 nan 0.1000 0.0006
## 60 0.4175 nan 0.1000 -0.0006
## 80 0.3599 nan 0.1000 -0.0010
## 100 0.3105 nan 0.1000 0.0001
## 120 0.2732 nan 0.1000 -0.0002
## 140 0.2367 nan 0.1000 -0.0009
## 160 0.2086 nan 0.1000 0.0000
## 180 0.1832 nan 0.1000 -0.0010
## 200 0.1601 nan 0.1000 -0.0003
## 220 0.1425 nan 0.1000 -0.0004
## 240 0.1258 nan 0.1000 -0.0000
## 260 0.1142 nan 0.1000 -0.0003
## 280 0.1034 nan 0.1000 -0.0002
## 300 0.0934 nan 0.1000 -0.0006
## 320 0.0843 nan 0.1000 -0.0000
## 340 0.0766 nan 0.1000 -0.0003
## 360 0.0691 nan 0.1000 -0.0001
## 380 0.0612 nan 0.1000 -0.0001
## 400 0.0557 nan 0.1000 -0.0002
## 420 0.0501 nan 0.1000 -0.0001
## 440 0.0452 nan 0.1000 -0.0002
## 460 0.0412 nan 0.1000 -0.0002
## 480 0.0374 nan 0.1000 -0.0002
## 500 0.0341 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0424
## 2 1.1634 nan 0.1000 0.0297
## 3 1.0947 nan 0.1000 0.0303
## 4 1.0381 nan 0.1000 0.0260
## 5 0.9853 nan 0.1000 0.0207
## 6 0.9425 nan 0.1000 0.0175
## 7 0.9032 nan 0.1000 0.0147
## 8 0.8719 nan 0.1000 0.0120
## 9 0.8412 nan 0.1000 0.0125
## 10 0.8199 nan 0.1000 0.0073
## 20 0.6567 nan 0.1000 0.0041
## 40 0.5128 nan 0.1000 -0.0006
## 60 0.4234 nan 0.1000 0.0004
## 80 0.3618 nan 0.1000 -0.0011
## 100 0.3160 nan 0.1000 -0.0009
## 120 0.2769 nan 0.1000 0.0002
## 140 0.2437 nan 0.1000 -0.0012
## 160 0.2150 nan 0.1000 0.0003
## 180 0.1885 nan 0.1000 -0.0008
## 200 0.1672 nan 0.1000 -0.0003
## 220 0.1512 nan 0.1000 -0.0005
## 240 0.1366 nan 0.1000 -0.0004
## 260 0.1239 nan 0.1000 -0.0007
## 280 0.1126 nan 0.1000 -0.0005
## 300 0.0990 nan 0.1000 -0.0006
## 320 0.0892 nan 0.1000 -0.0002
## 340 0.0806 nan 0.1000 -0.0005
## 360 0.0728 nan 0.1000 -0.0003
## 380 0.0661 nan 0.1000 -0.0003
## 400 0.0604 nan 0.1000 -0.0006
## 420 0.0552 nan 0.1000 -0.0002
## 440 0.0493 nan 0.1000 -0.0001
## 460 0.0441 nan 0.1000 -0.0002
## 480 0.0396 nan 0.1000 -0.0001
## 500 0.0358 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2255 nan 0.1000 0.0440
## 2 1.1555 nan 0.1000 0.0312
## 3 1.0908 nan 0.1000 0.0280
## 4 1.0356 nan 0.1000 0.0232
## 5 0.9901 nan 0.1000 0.0201
## 6 0.9504 nan 0.1000 0.0152
## 7 0.9130 nan 0.1000 0.0170
## 8 0.8796 nan 0.1000 0.0154
## 9 0.8502 nan 0.1000 0.0118
## 10 0.8266 nan 0.1000 0.0078
## 20 0.6706 nan 0.1000 0.0044
## 40 0.5387 nan 0.1000 -0.0010
## 60 0.4603 nan 0.1000 -0.0013
## 80 0.3815 nan 0.1000 -0.0001
## 100 0.3247 nan 0.1000 -0.0013
## 120 0.2812 nan 0.1000 -0.0001
## 140 0.2488 nan 0.1000 -0.0011
## 160 0.2181 nan 0.1000 -0.0001
## 180 0.1931 nan 0.1000 -0.0006
## 200 0.1731 nan 0.1000 -0.0010
## 220 0.1530 nan 0.1000 -0.0001
## 240 0.1374 nan 0.1000 -0.0008
## 260 0.1221 nan 0.1000 -0.0003
## 280 0.1092 nan 0.1000 -0.0002
## 300 0.0986 nan 0.1000 -0.0001
## 320 0.0887 nan 0.1000 -0.0000
## 340 0.0801 nan 0.1000 -0.0004
## 360 0.0724 nan 0.1000 -0.0003
## 380 0.0651 nan 0.1000 -0.0003
## 400 0.0595 nan 0.1000 -0.0001
## 420 0.0545 nan 0.1000 -0.0001
## 440 0.0496 nan 0.1000 -0.0003
## 460 0.0453 nan 0.1000 -0.0002
## 480 0.0407 nan 0.1000 -0.0001
## 500 0.0369 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0417
## 2 1.1512 nan 0.1000 0.0340
## 3 1.0809 nan 0.1000 0.0296
## 4 1.0205 nan 0.1000 0.0230
## 5 0.9744 nan 0.1000 0.0204
## 6 0.9341 nan 0.1000 0.0152
## 7 0.8929 nan 0.1000 0.0196
## 8 0.8646 nan 0.1000 0.0101
## 9 0.8326 nan 0.1000 0.0120
## 10 0.8052 nan 0.1000 0.0103
## 20 0.6331 nan 0.1000 0.0027
## 40 0.4766 nan 0.1000 -0.0007
## 60 0.3830 nan 0.1000 0.0011
## 80 0.3156 nan 0.1000 -0.0008
## 100 0.2633 nan 0.1000 -0.0002
## 120 0.2226 nan 0.1000 -0.0000
## 140 0.1930 nan 0.1000 -0.0004
## 160 0.1662 nan 0.1000 -0.0009
## 180 0.1441 nan 0.1000 -0.0000
## 200 0.1272 nan 0.1000 -0.0004
## 220 0.1101 nan 0.1000 -0.0004
## 240 0.0956 nan 0.1000 -0.0003
## 260 0.0844 nan 0.1000 -0.0002
## 280 0.0741 nan 0.1000 -0.0001
## 300 0.0654 nan 0.1000 -0.0002
## 320 0.0569 nan 0.1000 -0.0001
## 340 0.0502 nan 0.1000 -0.0001
## 360 0.0446 nan 0.1000 -0.0001
## 380 0.0393 nan 0.1000 -0.0001
## 400 0.0351 nan 0.1000 -0.0001
## 420 0.0319 nan 0.1000 -0.0001
## 440 0.0288 nan 0.1000 -0.0001
## 460 0.0252 nan 0.1000 -0.0001
## 480 0.0226 nan 0.1000 -0.0000
## 500 0.0200 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0378
## 2 1.1574 nan 0.1000 0.0339
## 3 1.0888 nan 0.1000 0.0318
## 4 1.0274 nan 0.1000 0.0282
## 5 0.9751 nan 0.1000 0.0238
## 6 0.9361 nan 0.1000 0.0150
## 7 0.8962 nan 0.1000 0.0158
## 8 0.8629 nan 0.1000 0.0136
## 9 0.8305 nan 0.1000 0.0114
## 10 0.8035 nan 0.1000 0.0081
## 20 0.6321 nan 0.1000 0.0015
## 40 0.4800 nan 0.1000 -0.0007
## 60 0.3893 nan 0.1000 -0.0005
## 80 0.3280 nan 0.1000 -0.0002
## 100 0.2727 nan 0.1000 -0.0007
## 120 0.2275 nan 0.1000 -0.0010
## 140 0.1918 nan 0.1000 -0.0005
## 160 0.1641 nan 0.1000 -0.0007
## 180 0.1404 nan 0.1000 -0.0007
## 200 0.1215 nan 0.1000 -0.0005
## 220 0.1061 nan 0.1000 -0.0008
## 240 0.0917 nan 0.1000 -0.0003
## 260 0.0801 nan 0.1000 -0.0003
## 280 0.0705 nan 0.1000 -0.0001
## 300 0.0616 nan 0.1000 -0.0003
## 320 0.0548 nan 0.1000 -0.0001
## 340 0.0476 nan 0.1000 -0.0001
## 360 0.0416 nan 0.1000 -0.0003
## 380 0.0369 nan 0.1000 -0.0002
## 400 0.0330 nan 0.1000 -0.0002
## 420 0.0291 nan 0.1000 -0.0000
## 440 0.0257 nan 0.1000 -0.0001
## 460 0.0226 nan 0.1000 -0.0000
## 480 0.0201 nan 0.1000 -0.0001
## 500 0.0178 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2331 nan 0.1000 0.0367
## 2 1.1535 nan 0.1000 0.0363
## 3 1.0821 nan 0.1000 0.0310
## 4 1.0267 nan 0.1000 0.0248
## 5 0.9776 nan 0.1000 0.0221
## 6 0.9358 nan 0.1000 0.0186
## 7 0.8964 nan 0.1000 0.0165
## 8 0.8613 nan 0.1000 0.0152
## 9 0.8321 nan 0.1000 0.0098
## 10 0.8041 nan 0.1000 0.0116
## 20 0.6353 nan 0.1000 0.0019
## 40 0.4888 nan 0.1000 -0.0007
## 60 0.3940 nan 0.1000 -0.0007
## 80 0.3318 nan 0.1000 -0.0006
## 100 0.2794 nan 0.1000 -0.0008
## 120 0.2377 nan 0.1000 -0.0010
## 140 0.2045 nan 0.1000 -0.0010
## 160 0.1763 nan 0.1000 -0.0012
## 180 0.1523 nan 0.1000 -0.0003
## 200 0.1315 nan 0.1000 -0.0008
## 220 0.1154 nan 0.1000 -0.0005
## 240 0.1024 nan 0.1000 -0.0002
## 260 0.0914 nan 0.1000 -0.0006
## 280 0.0814 nan 0.1000 -0.0003
## 300 0.0711 nan 0.1000 -0.0000
## 320 0.0632 nan 0.1000 -0.0002
## 340 0.0561 nan 0.1000 -0.0001
## 360 0.0503 nan 0.1000 -0.0002
## 380 0.0447 nan 0.1000 -0.0002
## 400 0.0397 nan 0.1000 -0.0001
## 420 0.0352 nan 0.1000 -0.0001
## 440 0.0314 nan 0.1000 -0.0001
## 460 0.0278 nan 0.1000 -0.0001
## 480 0.0249 nan 0.1000 -0.0002
## 500 0.0225 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3191 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2876 nan 0.0010 0.0004
## 60 1.2717 nan 0.0010 0.0003
## 80 1.2566 nan 0.0010 0.0003
## 100 1.2423 nan 0.0010 0.0002
## 120 1.2281 nan 0.0010 0.0003
## 140 1.2143 nan 0.0010 0.0003
## 160 1.2013 nan 0.0010 0.0003
## 180 1.1885 nan 0.0010 0.0003
## 200 1.1762 nan 0.0010 0.0003
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1518 nan 0.0010 0.0003
## 260 1.1402 nan 0.0010 0.0003
## 280 1.1289 nan 0.0010 0.0003
## 300 1.1180 nan 0.0010 0.0002
## 320 1.1072 nan 0.0010 0.0002
## 340 1.0970 nan 0.0010 0.0002
## 360 1.0870 nan 0.0010 0.0002
## 380 1.0773 nan 0.0010 0.0002
## 400 1.0677 nan 0.0010 0.0002
## 420 1.0587 nan 0.0010 0.0002
## 440 1.0495 nan 0.0010 0.0002
## 460 1.0409 nan 0.0010 0.0002
## 480 1.0324 nan 0.0010 0.0002
## 500 1.0240 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0003
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3036 nan 0.0010 0.0003
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2721 nan 0.0010 0.0003
## 80 1.2567 nan 0.0010 0.0004
## 100 1.2421 nan 0.0010 0.0003
## 120 1.2282 nan 0.0010 0.0003
## 140 1.2145 nan 0.0010 0.0003
## 160 1.2010 nan 0.0010 0.0003
## 180 1.1880 nan 0.0010 0.0003
## 200 1.1756 nan 0.0010 0.0002
## 220 1.1636 nan 0.0010 0.0002
## 240 1.1517 nan 0.0010 0.0003
## 260 1.1405 nan 0.0010 0.0003
## 280 1.1294 nan 0.0010 0.0003
## 300 1.1186 nan 0.0010 0.0002
## 320 1.1083 nan 0.0010 0.0002
## 340 1.0982 nan 0.0010 0.0002
## 360 1.0881 nan 0.0010 0.0002
## 380 1.0782 nan 0.0010 0.0002
## 400 1.0688 nan 0.0010 0.0002
## 420 1.0598 nan 0.0010 0.0002
## 440 1.0507 nan 0.0010 0.0002
## 460 1.0418 nan 0.0010 0.0002
## 480 1.0334 nan 0.0010 0.0002
## 500 1.0250 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3183 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0003
## 40 1.2883 nan 0.0010 0.0003
## 60 1.2731 nan 0.0010 0.0003
## 80 1.2579 nan 0.0010 0.0004
## 100 1.2434 nan 0.0010 0.0003
## 120 1.2295 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2029 nan 0.0010 0.0003
## 180 1.1900 nan 0.0010 0.0003
## 200 1.1776 nan 0.0010 0.0003
## 220 1.1654 nan 0.0010 0.0003
## 240 1.1537 nan 0.0010 0.0003
## 260 1.1424 nan 0.0010 0.0002
## 280 1.1314 nan 0.0010 0.0002
## 300 1.1206 nan 0.0010 0.0003
## 320 1.1099 nan 0.0010 0.0002
## 340 1.0995 nan 0.0010 0.0002
## 360 1.0896 nan 0.0010 0.0002
## 380 1.0800 nan 0.0010 0.0002
## 400 1.0708 nan 0.0010 0.0002
## 420 1.0615 nan 0.0010 0.0002
## 440 1.0525 nan 0.0010 0.0002
## 460 1.0438 nan 0.0010 0.0002
## 480 1.0351 nan 0.0010 0.0002
## 500 1.0268 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2687 nan 0.0010 0.0004
## 80 1.2524 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2220 nan 0.0010 0.0003
## 140 1.2077 nan 0.0010 0.0003
## 160 1.1936 nan 0.0010 0.0003
## 180 1.1799 nan 0.0010 0.0003
## 200 1.1668 nan 0.0010 0.0003
## 220 1.1541 nan 0.0010 0.0002
## 240 1.1418 nan 0.0010 0.0003
## 260 1.1298 nan 0.0010 0.0003
## 280 1.1180 nan 0.0010 0.0002
## 300 1.1063 nan 0.0010 0.0002
## 320 1.0952 nan 0.0010 0.0002
## 340 1.0846 nan 0.0010 0.0002
## 360 1.0741 nan 0.0010 0.0002
## 380 1.0641 nan 0.0010 0.0002
## 400 1.0543 nan 0.0010 0.0002
## 420 1.0443 nan 0.0010 0.0002
## 440 1.0347 nan 0.0010 0.0002
## 460 1.0254 nan 0.0010 0.0002
## 480 1.0165 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2857 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2532 nan 0.0010 0.0003
## 100 1.2378 nan 0.0010 0.0004
## 120 1.2229 nan 0.0010 0.0003
## 140 1.2084 nan 0.0010 0.0003
## 160 1.1944 nan 0.0010 0.0003
## 180 1.1809 nan 0.0010 0.0003
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## 220 1.1548 nan 0.0010 0.0003
## 240 1.1425 nan 0.0010 0.0003
## 260 1.1305 nan 0.0010 0.0002
## 280 1.1186 nan 0.0010 0.0003
## 300 1.1072 nan 0.0010 0.0003
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## 340 1.0853 nan 0.0010 0.0002
## 360 1.0748 nan 0.0010 0.0002
## 380 1.0644 nan 0.0010 0.0002
## 400 1.0547 nan 0.0010 0.0002
## 420 1.0450 nan 0.0010 0.0002
## 440 1.0356 nan 0.0010 0.0002
## 460 1.0263 nan 0.0010 0.0002
## 480 1.0174 nan 0.0010 0.0002
## 500 1.0088 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0003
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0003
## 60 1.2700 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1963 nan 0.0010 0.0003
## 180 1.1830 nan 0.0010 0.0003
## 200 1.1698 nan 0.0010 0.0003
## 220 1.1572 nan 0.0010 0.0003
## 240 1.1448 nan 0.0010 0.0002
## 260 1.1327 nan 0.0010 0.0003
## 280 1.1208 nan 0.0010 0.0003
## 300 1.1093 nan 0.0010 0.0003
## 320 1.0983 nan 0.0010 0.0002
## 340 1.0876 nan 0.0010 0.0002
## 360 1.0771 nan 0.0010 0.0002
## 380 1.0670 nan 0.0010 0.0002
## 400 1.0570 nan 0.0010 0.0002
## 420 1.0477 nan 0.0010 0.0002
## 440 1.0384 nan 0.0010 0.0002
## 460 1.0292 nan 0.0010 0.0002
## 480 1.0203 nan 0.0010 0.0002
## 500 1.0116 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2670 nan 0.0010 0.0004
## 80 1.2502 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2184 nan 0.0010 0.0003
## 140 1.2026 nan 0.0010 0.0003
## 160 1.1879 nan 0.0010 0.0003
## 180 1.1737 nan 0.0010 0.0003
## 200 1.1600 nan 0.0010 0.0003
## 220 1.1464 nan 0.0010 0.0003
## 240 1.1335 nan 0.0010 0.0003
## 260 1.1206 nan 0.0010 0.0002
## 280 1.1082 nan 0.0010 0.0002
## 300 1.0963 nan 0.0010 0.0002
## 320 1.0847 nan 0.0010 0.0003
## 340 1.0733 nan 0.0010 0.0003
## 360 1.0622 nan 0.0010 0.0002
## 380 1.0516 nan 0.0010 0.0002
## 400 1.0410 nan 0.0010 0.0002
## 420 1.0307 nan 0.0010 0.0003
## 440 1.0210 nan 0.0010 0.0002
## 460 1.0114 nan 0.0010 0.0002
## 480 1.0020 nan 0.0010 0.0002
## 500 0.9928 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2841 nan 0.0010 0.0004
## 60 1.2670 nan 0.0010 0.0004
## 80 1.2506 nan 0.0010 0.0004
## 100 1.2344 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2040 nan 0.0010 0.0003
## 160 1.1892 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1612 nan 0.0010 0.0003
## 220 1.1478 nan 0.0010 0.0003
## 240 1.1349 nan 0.0010 0.0003
## 260 1.1224 nan 0.0010 0.0003
## 280 1.1103 nan 0.0010 0.0003
## 300 1.0984 nan 0.0010 0.0003
## 320 1.0867 nan 0.0010 0.0002
## 340 1.0757 nan 0.0010 0.0002
## 360 1.0646 nan 0.0010 0.0002
## 380 1.0542 nan 0.0010 0.0002
## 400 1.0438 nan 0.0010 0.0002
## 420 1.0337 nan 0.0010 0.0002
## 440 1.0240 nan 0.0010 0.0002
## 460 1.0144 nan 0.0010 0.0002
## 480 1.0049 nan 0.0010 0.0002
## 500 0.9958 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2849 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0004
## 80 1.2517 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2202 nan 0.0010 0.0003
## 140 1.2054 nan 0.0010 0.0003
## 160 1.1913 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0003
## 200 1.1639 nan 0.0010 0.0003
## 220 1.1509 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0003
## 260 1.1259 nan 0.0010 0.0002
## 280 1.1136 nan 0.0010 0.0003
## 300 1.1019 nan 0.0010 0.0002
## 320 1.0905 nan 0.0010 0.0003
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0686 nan 0.0010 0.0002
## 380 1.0578 nan 0.0010 0.0002
## 400 1.0475 nan 0.0010 0.0002
## 420 1.0375 nan 0.0010 0.0002
## 440 1.0277 nan 0.0010 0.0002
## 460 1.0185 nan 0.0010 0.0002
## 480 1.0091 nan 0.0010 0.0002
## 500 1.0000 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0040
## 2 1.3020 nan 0.0100 0.0040
## 3 1.2941 nan 0.0100 0.0036
## 4 1.2858 nan 0.0100 0.0037
## 5 1.2780 nan 0.0100 0.0034
## 6 1.2700 nan 0.0100 0.0036
## 7 1.2621 nan 0.0100 0.0034
## 8 1.2556 nan 0.0100 0.0031
## 9 1.2482 nan 0.0100 0.0032
## 10 1.2410 nan 0.0100 0.0034
## 20 1.1745 nan 0.0100 0.0026
## 40 1.0684 nan 0.0100 0.0023
## 60 0.9852 nan 0.0100 0.0017
## 80 0.9191 nan 0.0100 0.0011
## 100 0.8686 nan 0.0100 0.0007
## 120 0.8268 nan 0.0100 0.0007
## 140 0.7922 nan 0.0100 0.0005
## 160 0.7620 nan 0.0100 0.0003
## 180 0.7360 nan 0.0100 0.0004
## 200 0.7137 nan 0.0100 0.0003
## 220 0.6938 nan 0.0100 0.0001
## 240 0.6761 nan 0.0100 0.0001
## 260 0.6596 nan 0.0100 -0.0001
## 280 0.6450 nan 0.0100 0.0002
## 300 0.6327 nan 0.0100 0.0000
## 320 0.6204 nan 0.0100 -0.0001
## 340 0.6086 nan 0.0100 -0.0001
## 360 0.5980 nan 0.0100 -0.0000
## 380 0.5882 nan 0.0100 -0.0000
## 400 0.5781 nan 0.0100 -0.0001
## 420 0.5684 nan 0.0100 -0.0001
## 440 0.5586 nan 0.0100 -0.0000
## 460 0.5505 nan 0.0100 -0.0001
## 480 0.5414 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0037
## 2 1.3038 nan 0.0100 0.0036
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2872 nan 0.0100 0.0036
## 5 1.2795 nan 0.0100 0.0036
## 6 1.2722 nan 0.0100 0.0032
## 7 1.2650 nan 0.0100 0.0032
## 8 1.2578 nan 0.0100 0.0029
## 9 1.2499 nan 0.0100 0.0035
## 10 1.2421 nan 0.0100 0.0037
## 20 1.1743 nan 0.0100 0.0030
## 40 1.0657 nan 0.0100 0.0020
## 60 0.9846 nan 0.0100 0.0013
## 80 0.9222 nan 0.0100 0.0011
## 100 0.8685 nan 0.0100 0.0009
## 120 0.8264 nan 0.0100 0.0006
## 140 0.7909 nan 0.0100 0.0005
## 160 0.7603 nan 0.0100 0.0004
## 180 0.7351 nan 0.0100 0.0001
## 200 0.7135 nan 0.0100 0.0002
## 220 0.6926 nan 0.0100 0.0002
## 240 0.6762 nan 0.0100 0.0001
## 260 0.6613 nan 0.0100 0.0002
## 280 0.6470 nan 0.0100 0.0001
## 300 0.6340 nan 0.0100 0.0000
## 320 0.6217 nan 0.0100 -0.0001
## 340 0.6102 nan 0.0100 -0.0001
## 360 0.6001 nan 0.0100 -0.0000
## 380 0.5892 nan 0.0100 -0.0000
## 400 0.5798 nan 0.0100 0.0000
## 420 0.5707 nan 0.0100 0.0000
## 440 0.5615 nan 0.0100 -0.0001
## 460 0.5530 nan 0.0100 0.0000
## 480 0.5447 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3125 nan 0.0100 0.0036
## 2 1.3048 nan 0.0100 0.0034
## 3 1.2963 nan 0.0100 0.0036
## 4 1.2881 nan 0.0100 0.0040
## 5 1.2807 nan 0.0100 0.0033
## 6 1.2724 nan 0.0100 0.0036
## 7 1.2643 nan 0.0100 0.0037
## 8 1.2574 nan 0.0100 0.0029
## 9 1.2496 nan 0.0100 0.0033
## 10 1.2421 nan 0.0100 0.0033
## 20 1.1765 nan 0.0100 0.0027
## 40 1.0693 nan 0.0100 0.0018
## 60 0.9904 nan 0.0100 0.0015
## 80 0.9269 nan 0.0100 0.0012
## 100 0.8742 nan 0.0100 0.0008
## 120 0.8303 nan 0.0100 0.0006
## 140 0.7943 nan 0.0100 0.0006
## 160 0.7647 nan 0.0100 0.0003
## 180 0.7394 nan 0.0100 0.0004
## 200 0.7176 nan 0.0100 0.0002
## 220 0.6992 nan 0.0100 0.0001
## 240 0.6816 nan 0.0100 0.0000
## 260 0.6663 nan 0.0100 0.0001
## 280 0.6528 nan 0.0100 0.0001
## 300 0.6398 nan 0.0100 0.0001
## 320 0.6283 nan 0.0100 -0.0000
## 340 0.6168 nan 0.0100 -0.0000
## 360 0.6063 nan 0.0100 -0.0000
## 380 0.5969 nan 0.0100 0.0001
## 400 0.5875 nan 0.0100 -0.0000
## 420 0.5782 nan 0.0100 0.0000
## 440 0.5690 nan 0.0100 -0.0000
## 460 0.5611 nan 0.0100 -0.0000
## 480 0.5529 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3025 nan 0.0100 0.0041
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2852 nan 0.0100 0.0036
## 5 1.2773 nan 0.0100 0.0034
## 6 1.2692 nan 0.0100 0.0035
## 7 1.2614 nan 0.0100 0.0039
## 8 1.2532 nan 0.0100 0.0033
## 9 1.2453 nan 0.0100 0.0036
## 10 1.2373 nan 0.0100 0.0037
## 20 1.1686 nan 0.0100 0.0028
## 40 1.0558 nan 0.0100 0.0019
## 60 0.9685 nan 0.0100 0.0018
## 80 0.8992 nan 0.0100 0.0012
## 100 0.8450 nan 0.0100 0.0007
## 120 0.8001 nan 0.0100 0.0005
## 140 0.7625 nan 0.0100 0.0004
## 160 0.7314 nan 0.0100 0.0002
## 180 0.7053 nan 0.0100 0.0003
## 200 0.6801 nan 0.0100 0.0004
## 220 0.6590 nan 0.0100 0.0001
## 240 0.6404 nan 0.0100 -0.0000
## 260 0.6228 nan 0.0100 0.0000
## 280 0.6070 nan 0.0100 -0.0000
## 300 0.5921 nan 0.0100 -0.0000
## 320 0.5774 nan 0.0100 -0.0000
## 340 0.5650 nan 0.0100 -0.0001
## 360 0.5531 nan 0.0100 -0.0000
## 380 0.5421 nan 0.0100 -0.0001
## 400 0.5312 nan 0.0100 0.0000
## 420 0.5197 nan 0.0100 -0.0000
## 440 0.5097 nan 0.0100 -0.0000
## 460 0.4997 nan 0.0100 -0.0001
## 480 0.4902 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0044
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2933 nan 0.0100 0.0044
## 4 1.2853 nan 0.0100 0.0034
## 5 1.2769 nan 0.0100 0.0037
## 6 1.2689 nan 0.0100 0.0040
## 7 1.2601 nan 0.0100 0.0038
## 8 1.2520 nan 0.0100 0.0036
## 9 1.2437 nan 0.0100 0.0035
## 10 1.2363 nan 0.0100 0.0033
## 20 1.1655 nan 0.0100 0.0029
## 40 1.0539 nan 0.0100 0.0022
## 60 0.9662 nan 0.0100 0.0015
## 80 0.8992 nan 0.0100 0.0011
## 100 0.8445 nan 0.0100 0.0011
## 120 0.8007 nan 0.0100 0.0005
## 140 0.7638 nan 0.0100 0.0005
## 160 0.7326 nan 0.0100 0.0005
## 180 0.7055 nan 0.0100 0.0003
## 200 0.6821 nan 0.0100 0.0003
## 220 0.6613 nan 0.0100 0.0001
## 240 0.6428 nan 0.0100 0.0003
## 260 0.6259 nan 0.0100 0.0002
## 280 0.6106 nan 0.0100 0.0002
## 300 0.5975 nan 0.0100 0.0002
## 320 0.5834 nan 0.0100 -0.0000
## 340 0.5711 nan 0.0100 0.0001
## 360 0.5588 nan 0.0100 -0.0001
## 380 0.5463 nan 0.0100 -0.0001
## 400 0.5366 nan 0.0100 -0.0001
## 420 0.5267 nan 0.0100 0.0000
## 440 0.5162 nan 0.0100 0.0000
## 460 0.5061 nan 0.0100 0.0001
## 480 0.4960 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0041
## 2 1.3025 nan 0.0100 0.0041
## 3 1.2945 nan 0.0100 0.0036
## 4 1.2864 nan 0.0100 0.0035
## 5 1.2780 nan 0.0100 0.0037
## 6 1.2693 nan 0.0100 0.0036
## 7 1.2617 nan 0.0100 0.0032
## 8 1.2546 nan 0.0100 0.0031
## 9 1.2476 nan 0.0100 0.0030
## 10 1.2403 nan 0.0100 0.0032
## 20 1.1691 nan 0.0100 0.0030
## 40 1.0578 nan 0.0100 0.0019
## 60 0.9729 nan 0.0100 0.0015
## 80 0.9041 nan 0.0100 0.0014
## 100 0.8495 nan 0.0100 0.0009
## 120 0.8059 nan 0.0100 0.0007
## 140 0.7689 nan 0.0100 0.0005
## 160 0.7353 nan 0.0100 0.0002
## 180 0.7103 nan 0.0100 0.0003
## 200 0.6877 nan 0.0100 0.0001
## 220 0.6674 nan 0.0100 0.0002
## 240 0.6491 nan 0.0100 0.0001
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## 280 0.6156 nan 0.0100 0.0002
## 300 0.6011 nan 0.0100 0.0000
## 320 0.5892 nan 0.0100 0.0001
## 340 0.5776 nan 0.0100 -0.0001
## 360 0.5669 nan 0.0100 0.0001
## 380 0.5548 nan 0.0100 0.0001
## 400 0.5447 nan 0.0100 -0.0001
## 420 0.5343 nan 0.0100 0.0000
## 440 0.5248 nan 0.0100 0.0001
## 460 0.5165 nan 0.0100 -0.0001
## 480 0.5074 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0044
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2934 nan 0.0100 0.0041
## 4 1.2845 nan 0.0100 0.0040
## 5 1.2760 nan 0.0100 0.0038
## 6 1.2671 nan 0.0100 0.0041
## 7 1.2582 nan 0.0100 0.0038
## 8 1.2501 nan 0.0100 0.0035
## 9 1.2423 nan 0.0100 0.0038
## 10 1.2338 nan 0.0100 0.0037
## 20 1.1617 nan 0.0100 0.0024
## 40 1.0422 nan 0.0100 0.0021
## 60 0.9512 nan 0.0100 0.0017
## 80 0.8787 nan 0.0100 0.0010
## 100 0.8225 nan 0.0100 0.0009
## 120 0.7744 nan 0.0100 0.0007
## 140 0.7355 nan 0.0100 0.0003
## 160 0.7026 nan 0.0100 0.0004
## 180 0.6736 nan 0.0100 0.0003
## 200 0.6478 nan 0.0100 0.0003
## 220 0.6246 nan 0.0100 0.0002
## 240 0.6037 nan 0.0100 0.0003
## 260 0.5862 nan 0.0100 0.0001
## 280 0.5688 nan 0.0100 -0.0000
## 300 0.5518 nan 0.0100 0.0001
## 320 0.5374 nan 0.0100 0.0001
## 340 0.5236 nan 0.0100 0.0001
## 360 0.5107 nan 0.0100 -0.0000
## 380 0.4982 nan 0.0100 -0.0001
## 400 0.4867 nan 0.0100 -0.0000
## 420 0.4751 nan 0.0100 -0.0000
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## 460 0.4540 nan 0.0100 0.0001
## 480 0.4445 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0042
## 2 1.3021 nan 0.0100 0.0041
## 3 1.2929 nan 0.0100 0.0040
## 4 1.2844 nan 0.0100 0.0040
## 5 1.2756 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0041
## 7 1.2585 nan 0.0100 0.0036
## 8 1.2500 nan 0.0100 0.0038
## 9 1.2423 nan 0.0100 0.0034
## 10 1.2340 nan 0.0100 0.0036
## 20 1.1611 nan 0.0100 0.0027
## 40 1.0418 nan 0.0100 0.0023
## 60 0.9507 nan 0.0100 0.0018
## 80 0.8803 nan 0.0100 0.0011
## 100 0.8247 nan 0.0100 0.0008
## 120 0.7783 nan 0.0100 0.0006
## 140 0.7382 nan 0.0100 0.0007
## 160 0.7058 nan 0.0100 0.0002
## 180 0.6775 nan 0.0100 0.0002
## 200 0.6512 nan 0.0100 0.0003
## 220 0.6285 nan 0.0100 0.0002
## 240 0.6078 nan 0.0100 0.0002
## 260 0.5898 nan 0.0100 0.0002
## 280 0.5738 nan 0.0100 -0.0001
## 300 0.5583 nan 0.0100 0.0001
## 320 0.5444 nan 0.0100 -0.0001
## 340 0.5325 nan 0.0100 -0.0001
## 360 0.5196 nan 0.0100 -0.0001
## 380 0.5078 nan 0.0100 -0.0001
## 400 0.4959 nan 0.0100 0.0000
## 420 0.4852 nan 0.0100 0.0000
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## 460 0.4652 nan 0.0100 -0.0001
## 480 0.4555 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0043
## 2 1.3022 nan 0.0100 0.0040
## 3 1.2926 nan 0.0100 0.0038
## 4 1.2837 nan 0.0100 0.0039
## 5 1.2749 nan 0.0100 0.0039
## 6 1.2663 nan 0.0100 0.0040
## 7 1.2578 nan 0.0100 0.0040
## 8 1.2498 nan 0.0100 0.0036
## 9 1.2415 nan 0.0100 0.0038
## 10 1.2335 nan 0.0100 0.0036
## 20 1.1595 nan 0.0100 0.0031
## 40 1.0460 nan 0.0100 0.0022
## 60 0.9569 nan 0.0100 0.0015
## 80 0.8875 nan 0.0100 0.0015
## 100 0.8322 nan 0.0100 0.0009
## 120 0.7857 nan 0.0100 0.0007
## 140 0.7480 nan 0.0100 0.0004
## 160 0.7154 nan 0.0100 0.0002
## 180 0.6872 nan 0.0100 0.0005
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## 260 0.6017 nan 0.0100 0.0003
## 280 0.5849 nan 0.0100 0.0001
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## 320 0.5553 nan 0.0100 -0.0001
## 340 0.5409 nan 0.0100 0.0001
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## 380 0.5163 nan 0.0100 0.0000
## 400 0.5041 nan 0.0100 -0.0001
## 420 0.4928 nan 0.0100 -0.0001
## 440 0.4825 nan 0.0100 0.0000
## 460 0.4730 nan 0.0100 -0.0001
## 480 0.4628 nan 0.0100 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2297 nan 0.1000 0.0421
## 2 1.1638 nan 0.1000 0.0294
## 3 1.1084 nan 0.1000 0.0219
## 4 1.0613 nan 0.1000 0.0222
## 5 1.0177 nan 0.1000 0.0181
## 6 0.9789 nan 0.1000 0.0173
## 7 0.9433 nan 0.1000 0.0127
## 8 0.9130 nan 0.1000 0.0111
## 9 0.8858 nan 0.1000 0.0098
## 10 0.8612 nan 0.1000 0.0086
## 20 0.7126 nan 0.1000 0.0013
## 40 0.5758 nan 0.1000 -0.0015
## 60 0.4973 nan 0.1000 0.0000
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## 120 0.3423 nan 0.1000 0.0001
## 140 0.3043 nan 0.1000 -0.0004
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## 180 0.2498 nan 0.1000 -0.0004
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## 220 0.2031 nan 0.1000 -0.0003
## 240 0.1838 nan 0.1000 -0.0006
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## 340 0.1189 nan 0.1000 -0.0002
## 360 0.1080 nan 0.1000 -0.0004
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## 460 0.0703 nan 0.1000 -0.0003
## 480 0.0656 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2380 nan 0.1000 0.0347
## 2 1.1687 nan 0.1000 0.0325
## 3 1.1158 nan 0.1000 0.0212
## 4 1.0686 nan 0.1000 0.0212
## 5 1.0154 nan 0.1000 0.0194
## 6 0.9678 nan 0.1000 0.0179
## 7 0.9325 nan 0.1000 0.0147
## 8 0.9008 nan 0.1000 0.0116
## 9 0.8752 nan 0.1000 0.0104
## 10 0.8519 nan 0.1000 0.0082
## 20 0.7013 nan 0.1000 0.0046
## 40 0.5657 nan 0.1000 -0.0008
## 60 0.4913 nan 0.1000 -0.0006
## 80 0.4356 nan 0.1000 0.0001
## 100 0.3878 nan 0.1000 -0.0019
## 120 0.3449 nan 0.1000 0.0001
## 140 0.3118 nan 0.1000 -0.0008
## 160 0.2800 nan 0.1000 -0.0017
## 180 0.2486 nan 0.1000 -0.0005
## 200 0.2271 nan 0.1000 -0.0006
## 220 0.2058 nan 0.1000 -0.0003
## 240 0.1857 nan 0.1000 -0.0004
## 260 0.1708 nan 0.1000 -0.0006
## 280 0.1561 nan 0.1000 -0.0007
## 300 0.1447 nan 0.1000 -0.0005
## 320 0.1332 nan 0.1000 -0.0001
## 340 0.1207 nan 0.1000 -0.0003
## 360 0.1119 nan 0.1000 -0.0002
## 380 0.1038 nan 0.1000 -0.0002
## 400 0.0964 nan 0.1000 -0.0001
## 420 0.0884 nan 0.1000 -0.0002
## 440 0.0826 nan 0.1000 -0.0002
## 460 0.0769 nan 0.1000 -0.0001
## 480 0.0710 nan 0.1000 -0.0002
## 500 0.0651 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0353
## 2 1.1711 nan 0.1000 0.0286
## 3 1.1163 nan 0.1000 0.0234
## 4 1.0634 nan 0.1000 0.0247
## 5 1.0209 nan 0.1000 0.0185
## 6 0.9785 nan 0.1000 0.0158
## 7 0.9494 nan 0.1000 0.0123
## 8 0.9170 nan 0.1000 0.0136
## 9 0.8907 nan 0.1000 0.0091
## 10 0.8653 nan 0.1000 0.0107
## 20 0.7169 nan 0.1000 0.0025
## 40 0.5883 nan 0.1000 -0.0003
## 60 0.5142 nan 0.1000 -0.0020
## 80 0.4600 nan 0.1000 -0.0013
## 100 0.4105 nan 0.1000 -0.0005
## 120 0.3670 nan 0.1000 0.0002
## 140 0.3302 nan 0.1000 0.0001
## 160 0.2980 nan 0.1000 -0.0005
## 180 0.2687 nan 0.1000 -0.0008
## 200 0.2452 nan 0.1000 -0.0009
## 220 0.2226 nan 0.1000 -0.0003
## 240 0.2044 nan 0.1000 -0.0010
## 260 0.1878 nan 0.1000 -0.0005
## 280 0.1728 nan 0.1000 -0.0007
## 300 0.1583 nan 0.1000 -0.0003
## 320 0.1454 nan 0.1000 -0.0006
## 340 0.1332 nan 0.1000 -0.0002
## 360 0.1232 nan 0.1000 -0.0003
## 380 0.1140 nan 0.1000 -0.0004
## 400 0.1054 nan 0.1000 -0.0002
## 420 0.0983 nan 0.1000 -0.0002
## 440 0.0912 nan 0.1000 -0.0003
## 460 0.0851 nan 0.1000 -0.0003
## 480 0.0784 nan 0.1000 -0.0001
## 500 0.0724 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2426 nan 0.1000 0.0358
## 2 1.1676 nan 0.1000 0.0341
## 3 1.1099 nan 0.1000 0.0262
## 4 1.0552 nan 0.1000 0.0247
## 5 1.0070 nan 0.1000 0.0219
## 6 0.9699 nan 0.1000 0.0137
## 7 0.9323 nan 0.1000 0.0150
## 8 0.8972 nan 0.1000 0.0159
## 9 0.8665 nan 0.1000 0.0106
## 10 0.8390 nan 0.1000 0.0116
## 20 0.6706 nan 0.1000 0.0014
## 40 0.5231 nan 0.1000 0.0002
## 60 0.4319 nan 0.1000 -0.0002
## 80 0.3595 nan 0.1000 -0.0004
## 100 0.3054 nan 0.1000 -0.0008
## 120 0.2664 nan 0.1000 -0.0006
## 140 0.2363 nan 0.1000 -0.0009
## 160 0.2061 nan 0.1000 -0.0006
## 180 0.1796 nan 0.1000 -0.0002
## 200 0.1614 nan 0.1000 -0.0004
## 220 0.1443 nan 0.1000 -0.0007
## 240 0.1289 nan 0.1000 -0.0000
## 260 0.1145 nan 0.1000 -0.0004
## 280 0.1019 nan 0.1000 -0.0002
## 300 0.0910 nan 0.1000 -0.0001
## 320 0.0824 nan 0.1000 -0.0003
## 340 0.0753 nan 0.1000 -0.0001
## 360 0.0676 nan 0.1000 -0.0003
## 380 0.0608 nan 0.1000 -0.0001
## 400 0.0557 nan 0.1000 -0.0002
## 420 0.0503 nan 0.1000 -0.0001
## 440 0.0451 nan 0.1000 -0.0001
## 460 0.0406 nan 0.1000 -0.0001
## 480 0.0366 nan 0.1000 -0.0000
## 500 0.0331 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2378 nan 0.1000 0.0400
## 2 1.1615 nan 0.1000 0.0321
## 3 1.0996 nan 0.1000 0.0264
## 4 1.0511 nan 0.1000 0.0221
## 5 1.0020 nan 0.1000 0.0210
## 6 0.9631 nan 0.1000 0.0154
## 7 0.9277 nan 0.1000 0.0171
## 8 0.8970 nan 0.1000 0.0135
## 9 0.8678 nan 0.1000 0.0101
## 10 0.8408 nan 0.1000 0.0099
## 20 0.6821 nan 0.1000 0.0039
## 40 0.5424 nan 0.1000 0.0003
## 60 0.4604 nan 0.1000 -0.0008
## 80 0.3896 nan 0.1000 -0.0008
## 100 0.3350 nan 0.1000 -0.0011
## 120 0.2931 nan 0.1000 -0.0012
## 140 0.2575 nan 0.1000 -0.0011
## 160 0.2269 nan 0.1000 -0.0015
## 180 0.1994 nan 0.1000 -0.0003
## 200 0.1777 nan 0.1000 -0.0005
## 220 0.1587 nan 0.1000 -0.0004
## 240 0.1420 nan 0.1000 -0.0010
## 260 0.1277 nan 0.1000 -0.0003
## 280 0.1141 nan 0.1000 -0.0003
## 300 0.1031 nan 0.1000 -0.0002
## 320 0.0935 nan 0.1000 -0.0001
## 340 0.0838 nan 0.1000 -0.0004
## 360 0.0769 nan 0.1000 -0.0001
## 380 0.0690 nan 0.1000 -0.0004
## 400 0.0619 nan 0.1000 -0.0001
## 420 0.0556 nan 0.1000 -0.0002
## 440 0.0507 nan 0.1000 -0.0002
## 460 0.0453 nan 0.1000 -0.0002
## 480 0.0413 nan 0.1000 -0.0001
## 500 0.0376 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0374
## 2 1.1695 nan 0.1000 0.0302
## 3 1.1104 nan 0.1000 0.0260
## 4 1.0621 nan 0.1000 0.0221
## 5 1.0150 nan 0.1000 0.0206
## 6 0.9722 nan 0.1000 0.0184
## 7 0.9344 nan 0.1000 0.0156
## 8 0.9000 nan 0.1000 0.0161
## 9 0.8755 nan 0.1000 0.0087
## 10 0.8502 nan 0.1000 0.0100
## 20 0.6953 nan 0.1000 0.0009
## 40 0.5505 nan 0.1000 -0.0013
## 60 0.4680 nan 0.1000 -0.0021
## 80 0.4042 nan 0.1000 -0.0001
## 100 0.3445 nan 0.1000 -0.0004
## 120 0.3017 nan 0.1000 -0.0001
## 140 0.2641 nan 0.1000 -0.0008
## 160 0.2343 nan 0.1000 -0.0009
## 180 0.2064 nan 0.1000 -0.0010
## 200 0.1851 nan 0.1000 -0.0008
## 220 0.1663 nan 0.1000 -0.0012
## 240 0.1481 nan 0.1000 -0.0001
## 260 0.1325 nan 0.1000 -0.0000
## 280 0.1196 nan 0.1000 -0.0002
## 300 0.1084 nan 0.1000 -0.0004
## 320 0.0983 nan 0.1000 -0.0004
## 340 0.0890 nan 0.1000 -0.0001
## 360 0.0811 nan 0.1000 -0.0003
## 380 0.0735 nan 0.1000 -0.0002
## 400 0.0658 nan 0.1000 -0.0003
## 420 0.0598 nan 0.1000 -0.0005
## 440 0.0545 nan 0.1000 -0.0002
## 460 0.0496 nan 0.1000 -0.0002
## 480 0.0455 nan 0.1000 -0.0003
## 500 0.0416 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0422
## 2 1.1549 nan 0.1000 0.0339
## 3 1.0931 nan 0.1000 0.0281
## 4 1.0332 nan 0.1000 0.0262
## 5 0.9899 nan 0.1000 0.0188
## 6 0.9524 nan 0.1000 0.0163
## 7 0.9108 nan 0.1000 0.0170
## 8 0.8768 nan 0.1000 0.0107
## 9 0.8490 nan 0.1000 0.0090
## 10 0.8207 nan 0.1000 0.0093
## 20 0.6518 nan 0.1000 0.0015
## 40 0.4941 nan 0.1000 -0.0005
## 60 0.3955 nan 0.1000 -0.0004
## 80 0.3235 nan 0.1000 -0.0007
## 100 0.2710 nan 0.1000 0.0003
## 120 0.2330 nan 0.1000 -0.0009
## 140 0.1964 nan 0.1000 -0.0006
## 160 0.1696 nan 0.1000 -0.0002
## 180 0.1466 nan 0.1000 -0.0007
## 200 0.1282 nan 0.1000 -0.0001
## 220 0.1113 nan 0.1000 -0.0003
## 240 0.0968 nan 0.1000 -0.0001
## 260 0.0849 nan 0.1000 -0.0006
## 280 0.0750 nan 0.1000 -0.0004
## 300 0.0654 nan 0.1000 -0.0002
## 320 0.0580 nan 0.1000 -0.0001
## 340 0.0508 nan 0.1000 -0.0001
## 360 0.0447 nan 0.1000 -0.0001
## 380 0.0402 nan 0.1000 -0.0001
## 400 0.0355 nan 0.1000 -0.0000
## 420 0.0314 nan 0.1000 -0.0001
## 440 0.0279 nan 0.1000 -0.0001
## 460 0.0249 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0001
## 500 0.0198 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0404
## 2 1.1547 nan 0.1000 0.0332
## 3 1.0949 nan 0.1000 0.0275
## 4 1.0395 nan 0.1000 0.0204
## 5 0.9935 nan 0.1000 0.0192
## 6 0.9530 nan 0.1000 0.0170
## 7 0.9154 nan 0.1000 0.0170
## 8 0.8823 nan 0.1000 0.0124
## 9 0.8497 nan 0.1000 0.0112
## 10 0.8229 nan 0.1000 0.0062
## 20 0.6575 nan 0.1000 0.0010
## 40 0.4956 nan 0.1000 -0.0004
## 60 0.4049 nan 0.1000 -0.0016
## 80 0.3380 nan 0.1000 -0.0012
## 100 0.2838 nan 0.1000 -0.0013
## 120 0.2401 nan 0.1000 -0.0010
## 140 0.2042 nan 0.1000 -0.0010
## 160 0.1759 nan 0.1000 -0.0008
## 180 0.1534 nan 0.1000 -0.0001
## 200 0.1334 nan 0.1000 -0.0006
## 220 0.1181 nan 0.1000 -0.0001
## 240 0.1042 nan 0.1000 -0.0004
## 260 0.0905 nan 0.1000 -0.0004
## 280 0.0796 nan 0.1000 -0.0001
## 300 0.0715 nan 0.1000 -0.0004
## 320 0.0630 nan 0.1000 -0.0001
## 340 0.0548 nan 0.1000 -0.0002
## 360 0.0485 nan 0.1000 -0.0003
## 380 0.0429 nan 0.1000 -0.0001
## 400 0.0376 nan 0.1000 -0.0000
## 420 0.0337 nan 0.1000 -0.0002
## 440 0.0300 nan 0.1000 -0.0000
## 460 0.0267 nan 0.1000 -0.0001
## 480 0.0239 nan 0.1000 -0.0002
## 500 0.0211 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2316 nan 0.1000 0.0393
## 2 1.1574 nan 0.1000 0.0311
## 3 1.0926 nan 0.1000 0.0271
## 4 1.0354 nan 0.1000 0.0210
## 5 0.9900 nan 0.1000 0.0206
## 6 0.9455 nan 0.1000 0.0184
## 7 0.9067 nan 0.1000 0.0178
## 8 0.8762 nan 0.1000 0.0097
## 9 0.8497 nan 0.1000 0.0081
## 10 0.8199 nan 0.1000 0.0101
## 20 0.6527 nan 0.1000 0.0007
## 40 0.5134 nan 0.1000 -0.0032
## 60 0.4165 nan 0.1000 -0.0002
## 80 0.3411 nan 0.1000 -0.0007
## 100 0.2878 nan 0.1000 0.0001
## 120 0.2473 nan 0.1000 -0.0006
## 140 0.2138 nan 0.1000 -0.0004
## 160 0.1866 nan 0.1000 -0.0014
## 180 0.1647 nan 0.1000 -0.0012
## 200 0.1439 nan 0.1000 -0.0003
## 220 0.1274 nan 0.1000 -0.0006
## 240 0.1114 nan 0.1000 -0.0009
## 260 0.0985 nan 0.1000 -0.0003
## 280 0.0853 nan 0.1000 -0.0001
## 300 0.0751 nan 0.1000 -0.0003
## 320 0.0675 nan 0.1000 -0.0001
## 340 0.0593 nan 0.1000 -0.0002
## 360 0.0532 nan 0.1000 -0.0003
## 380 0.0481 nan 0.1000 -0.0001
## 400 0.0428 nan 0.1000 -0.0002
## 420 0.0382 nan 0.1000 -0.0003
## 440 0.0342 nan 0.1000 -0.0001
## 460 0.0308 nan 0.1000 -0.0002
## 480 0.0280 nan 0.1000 -0.0002
## 500 0.0252 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2101 nan 0.0010 0.0003
## 160 1.1960 nan 0.0010 0.0003
## 180 1.1827 nan 0.0010 0.0003
## 200 1.1697 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1447 nan 0.0010 0.0003
## 260 1.1330 nan 0.0010 0.0003
## 280 1.1213 nan 0.0010 0.0003
## 300 1.1104 nan 0.0010 0.0002
## 320 1.0997 nan 0.0010 0.0002
## 340 1.0892 nan 0.0010 0.0003
## 360 1.0785 nan 0.0010 0.0002
## 380 1.0686 nan 0.0010 0.0002
## 400 1.0590 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0002
## 440 1.0405 nan 0.0010 0.0002
## 460 1.0314 nan 0.0010 0.0002
## 480 1.0227 nan 0.0010 0.0002
## 500 1.0143 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2544 nan 0.0010 0.0003
## 100 1.2391 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2104 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1832 nan 0.0010 0.0003
## 200 1.1704 nan 0.0010 0.0003
## 220 1.1579 nan 0.0010 0.0003
## 240 1.1459 nan 0.0010 0.0003
## 260 1.1341 nan 0.0010 0.0003
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1118 nan 0.0010 0.0002
## 320 1.1010 nan 0.0010 0.0002
## 340 1.0904 nan 0.0010 0.0002
## 360 1.0800 nan 0.0010 0.0002
## 380 1.0700 nan 0.0010 0.0002
## 400 1.0602 nan 0.0010 0.0002
## 420 1.0509 nan 0.0010 0.0002
## 440 1.0421 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
## 500 1.0161 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2552 nan 0.0010 0.0003
## 100 1.2404 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2116 nan 0.0010 0.0003
## 160 1.1982 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1721 nan 0.0010 0.0003
## 220 1.1599 nan 0.0010 0.0003
## 240 1.1479 nan 0.0010 0.0003
## 260 1.1364 nan 0.0010 0.0002
## 280 1.1253 nan 0.0010 0.0003
## 300 1.1141 nan 0.0010 0.0002
## 320 1.1032 nan 0.0010 0.0002
## 340 1.0928 nan 0.0010 0.0002
## 360 1.0827 nan 0.0010 0.0002
## 380 1.0729 nan 0.0010 0.0002
## 400 1.0634 nan 0.0010 0.0002
## 420 1.0541 nan 0.0010 0.0002
## 440 1.0450 nan 0.0010 0.0002
## 460 1.0361 nan 0.0010 0.0002
## 480 1.0273 nan 0.0010 0.0002
## 500 1.0190 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0003
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0004
## 100 1.2346 nan 0.0010 0.0003
## 120 1.2191 nan 0.0010 0.0004
## 140 1.2039 nan 0.0010 0.0004
## 160 1.1892 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
## 200 1.1617 nan 0.0010 0.0003
## 220 1.1482 nan 0.0010 0.0003
## 240 1.1352 nan 0.0010 0.0003
## 260 1.1226 nan 0.0010 0.0003
## 280 1.1106 nan 0.0010 0.0002
## 300 1.0988 nan 0.0010 0.0003
## 320 1.0872 nan 0.0010 0.0003
## 340 1.0763 nan 0.0010 0.0002
## 360 1.0655 nan 0.0010 0.0002
## 380 1.0550 nan 0.0010 0.0002
## 400 1.0446 nan 0.0010 0.0002
## 420 1.0346 nan 0.0010 0.0002
## 440 1.0251 nan 0.0010 0.0002
## 460 1.0156 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
## 500 0.9977 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0005
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2673 nan 0.0010 0.0004
## 80 1.2507 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0004
## 120 1.2195 nan 0.0010 0.0003
## 140 1.2046 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0004
## 180 1.1762 nan 0.0010 0.0003
## 200 1.1625 nan 0.0010 0.0003
## 220 1.1495 nan 0.0010 0.0003
## 240 1.1367 nan 0.0010 0.0002
## 260 1.1244 nan 0.0010 0.0003
## 280 1.1126 nan 0.0010 0.0003
## 300 1.1007 nan 0.0010 0.0003
## 320 1.0891 nan 0.0010 0.0002
## 340 1.0780 nan 0.0010 0.0003
## 360 1.0674 nan 0.0010 0.0002
## 380 1.0569 nan 0.0010 0.0002
## 400 1.0466 nan 0.0010 0.0002
## 420 1.0369 nan 0.0010 0.0002
## 440 1.0272 nan 0.0010 0.0002
## 460 1.0179 nan 0.0010 0.0002
## 480 1.0089 nan 0.0010 0.0002
## 500 1.0001 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2859 nan 0.0010 0.0004
## 60 1.2693 nan 0.0010 0.0004
## 80 1.2529 nan 0.0010 0.0004
## 100 1.2372 nan 0.0010 0.0003
## 120 1.2220 nan 0.0010 0.0004
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1933 nan 0.0010 0.0003
## 180 1.1796 nan 0.0010 0.0003
## 200 1.1660 nan 0.0010 0.0003
## 220 1.1532 nan 0.0010 0.0003
## 240 1.1403 nan 0.0010 0.0003
## 260 1.1281 nan 0.0010 0.0003
## 280 1.1162 nan 0.0010 0.0003
## 300 1.1048 nan 0.0010 0.0002
## 320 1.0936 nan 0.0010 0.0002
## 340 1.0826 nan 0.0010 0.0002
## 360 1.0720 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0512 nan 0.0010 0.0002
## 420 1.0413 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0225 nan 0.0010 0.0002
## 480 1.0135 nan 0.0010 0.0002
## 500 1.0048 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0004
## 20 1.3012 nan 0.0010 0.0004
## 40 1.2823 nan 0.0010 0.0004
## 60 1.2640 nan 0.0010 0.0004
## 80 1.2466 nan 0.0010 0.0003
## 100 1.2299 nan 0.0010 0.0004
## 120 1.2135 nan 0.0010 0.0004
## 140 1.1975 nan 0.0010 0.0003
## 160 1.1825 nan 0.0010 0.0004
## 180 1.1680 nan 0.0010 0.0003
## 200 1.1538 nan 0.0010 0.0003
## 220 1.1401 nan 0.0010 0.0003
## 240 1.1266 nan 0.0010 0.0003
## 260 1.1138 nan 0.0010 0.0003
## 280 1.1013 nan 0.0010 0.0002
## 300 1.0890 nan 0.0010 0.0003
## 320 1.0774 nan 0.0010 0.0002
## 340 1.0658 nan 0.0010 0.0002
## 360 1.0545 nan 0.0010 0.0002
## 380 1.0435 nan 0.0010 0.0002
## 400 1.0330 nan 0.0010 0.0002
## 420 1.0227 nan 0.0010 0.0002
## 440 1.0126 nan 0.0010 0.0002
## 460 1.0028 nan 0.0010 0.0002
## 480 0.9932 nan 0.0010 0.0002
## 500 0.9840 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0005
## 20 1.3016 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0004
## 60 1.2655 nan 0.0010 0.0004
## 80 1.2482 nan 0.0010 0.0004
## 100 1.2317 nan 0.0010 0.0004
## 120 1.2155 nan 0.0010 0.0004
## 140 1.2002 nan 0.0010 0.0003
## 160 1.1851 nan 0.0010 0.0003
## 180 1.1707 nan 0.0010 0.0003
## 200 1.1566 nan 0.0010 0.0003
## 220 1.1428 nan 0.0010 0.0003
## 240 1.1294 nan 0.0010 0.0003
## 260 1.1165 nan 0.0010 0.0003
## 280 1.1042 nan 0.0010 0.0002
## 300 1.0918 nan 0.0010 0.0003
## 320 1.0801 nan 0.0010 0.0002
## 340 1.0687 nan 0.0010 0.0003
## 360 1.0573 nan 0.0010 0.0002
## 380 1.0466 nan 0.0010 0.0002
## 400 1.0362 nan 0.0010 0.0002
## 420 1.0260 nan 0.0010 0.0002
## 440 1.0160 nan 0.0010 0.0002
## 460 1.0063 nan 0.0010 0.0002
## 480 0.9968 nan 0.0010 0.0002
## 500 0.9877 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2335 nan 0.0010 0.0004
## 120 1.2177 nan 0.0010 0.0003
## 140 1.2026 nan 0.0010 0.0004
## 160 1.1880 nan 0.0010 0.0003
## 180 1.1735 nan 0.0010 0.0003
## 200 1.1596 nan 0.0010 0.0003
## 220 1.1460 nan 0.0010 0.0003
## 240 1.1330 nan 0.0010 0.0003
## 260 1.1201 nan 0.0010 0.0003
## 280 1.1077 nan 0.0010 0.0003
## 300 1.0961 nan 0.0010 0.0002
## 320 1.0847 nan 0.0010 0.0002
## 340 1.0734 nan 0.0010 0.0002
## 360 1.0626 nan 0.0010 0.0002
## 380 1.0516 nan 0.0010 0.0003
## 400 1.0413 nan 0.0010 0.0002
## 420 1.0314 nan 0.0010 0.0002
## 440 1.0215 nan 0.0010 0.0002
## 460 1.0120 nan 0.0010 0.0002
## 480 1.0026 nan 0.0010 0.0002
## 500 0.9937 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0034
## 2 1.3038 nan 0.0100 0.0041
## 3 1.2946 nan 0.0100 0.0043
## 4 1.2858 nan 0.0100 0.0038
## 5 1.2778 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0041
## 7 1.2609 nan 0.0100 0.0039
## 8 1.2536 nan 0.0100 0.0032
## 9 1.2456 nan 0.0100 0.0038
## 10 1.2383 nan 0.0100 0.0033
## 20 1.1696 nan 0.0100 0.0023
## 40 1.0596 nan 0.0100 0.0020
## 60 0.9749 nan 0.0100 0.0015
## 80 0.9086 nan 0.0100 0.0011
## 100 0.8551 nan 0.0100 0.0011
## 120 0.8111 nan 0.0100 0.0005
## 140 0.7764 nan 0.0100 0.0005
## 160 0.7466 nan 0.0100 0.0003
## 180 0.7220 nan 0.0100 0.0002
## 200 0.7003 nan 0.0100 0.0002
## 220 0.6801 nan 0.0100 0.0001
## 240 0.6637 nan 0.0100 0.0001
## 260 0.6480 nan 0.0100 -0.0000
## 280 0.6340 nan 0.0100 0.0001
## 300 0.6208 nan 0.0100 0.0001
## 320 0.6095 nan 0.0100 0.0001
## 340 0.5993 nan 0.0100 -0.0000
## 360 0.5889 nan 0.0100 -0.0001
## 380 0.5786 nan 0.0100 -0.0002
## 400 0.5689 nan 0.0100 0.0000
## 420 0.5600 nan 0.0100 -0.0000
## 440 0.5506 nan 0.0100 0.0001
## 460 0.5416 nan 0.0100 -0.0000
## 480 0.5336 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3026 nan 0.0100 0.0040
## 3 1.2943 nan 0.0100 0.0038
## 4 1.2866 nan 0.0100 0.0035
## 5 1.2775 nan 0.0100 0.0041
## 6 1.2691 nan 0.0100 0.0037
## 7 1.2618 nan 0.0100 0.0033
## 8 1.2542 nan 0.0100 0.0035
## 9 1.2463 nan 0.0100 0.0035
## 10 1.2388 nan 0.0100 0.0036
## 20 1.1700 nan 0.0100 0.0032
## 40 1.0600 nan 0.0100 0.0018
## 60 0.9775 nan 0.0100 0.0017
## 80 0.9119 nan 0.0100 0.0009
## 100 0.8589 nan 0.0100 0.0008
## 120 0.8172 nan 0.0100 0.0007
## 140 0.7813 nan 0.0100 0.0005
## 160 0.7530 nan 0.0100 0.0003
## 180 0.7288 nan 0.0100 0.0003
## 200 0.7081 nan 0.0100 0.0003
## 220 0.6880 nan 0.0100 0.0001
## 240 0.6711 nan 0.0100 0.0001
## 260 0.6553 nan 0.0100 0.0001
## 280 0.6422 nan 0.0100 0.0001
## 300 0.6297 nan 0.0100 0.0001
## 320 0.6174 nan 0.0100 0.0001
## 340 0.6061 nan 0.0100 0.0000
## 360 0.5956 nan 0.0100 0.0000
## 380 0.5856 nan 0.0100 0.0000
## 400 0.5755 nan 0.0100 -0.0001
## 420 0.5666 nan 0.0100 0.0001
## 440 0.5582 nan 0.0100 -0.0001
## 460 0.5495 nan 0.0100 0.0000
## 480 0.5411 nan 0.0100 -0.0000
## 500 0.5340 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0037
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2868 nan 0.0100 0.0034
## 5 1.2789 nan 0.0100 0.0034
## 6 1.2714 nan 0.0100 0.0036
## 7 1.2642 nan 0.0100 0.0034
## 8 1.2567 nan 0.0100 0.0029
## 9 1.2495 nan 0.0100 0.0034
## 10 1.2419 nan 0.0100 0.0031
## 20 1.1708 nan 0.0100 0.0030
## 40 1.0609 nan 0.0100 0.0020
## 60 0.9789 nan 0.0100 0.0015
## 80 0.9144 nan 0.0100 0.0012
## 100 0.8630 nan 0.0100 0.0007
## 120 0.8212 nan 0.0100 0.0007
## 140 0.7876 nan 0.0100 0.0004
## 160 0.7594 nan 0.0100 0.0004
## 180 0.7353 nan 0.0100 0.0003
## 200 0.7133 nan 0.0100 0.0003
## 220 0.6948 nan 0.0100 0.0001
## 240 0.6778 nan 0.0100 0.0001
## 260 0.6620 nan 0.0100 0.0001
## 280 0.6482 nan 0.0100 0.0001
## 300 0.6360 nan 0.0100 0.0000
## 320 0.6251 nan 0.0100 0.0000
## 340 0.6138 nan 0.0100 0.0000
## 360 0.6042 nan 0.0100 -0.0001
## 380 0.5934 nan 0.0100 -0.0000
## 400 0.5841 nan 0.0100 0.0000
## 420 0.5752 nan 0.0100 0.0001
## 440 0.5672 nan 0.0100 -0.0001
## 460 0.5592 nan 0.0100 0.0000
## 480 0.5515 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3019 nan 0.0100 0.0043
## 3 1.2930 nan 0.0100 0.0040
## 4 1.2844 nan 0.0100 0.0038
## 5 1.2755 nan 0.0100 0.0042
## 6 1.2667 nan 0.0100 0.0040
## 7 1.2581 nan 0.0100 0.0040
## 8 1.2497 nan 0.0100 0.0041
## 9 1.2412 nan 0.0100 0.0035
## 10 1.2337 nan 0.0100 0.0030
## 20 1.1590 nan 0.0100 0.0032
## 40 1.0439 nan 0.0100 0.0022
## 60 0.9549 nan 0.0100 0.0014
## 80 0.8858 nan 0.0100 0.0012
## 100 0.8317 nan 0.0100 0.0009
## 120 0.7865 nan 0.0100 0.0009
## 140 0.7492 nan 0.0100 0.0004
## 160 0.7189 nan 0.0100 0.0005
## 180 0.6910 nan 0.0100 0.0001
## 200 0.6670 nan 0.0100 0.0003
## 220 0.6448 nan 0.0100 0.0000
## 240 0.6262 nan 0.0100 0.0003
## 260 0.6092 nan 0.0100 0.0002
## 280 0.5947 nan 0.0100 -0.0003
## 300 0.5819 nan 0.0100 0.0000
## 320 0.5688 nan 0.0100 -0.0000
## 340 0.5566 nan 0.0100 0.0000
## 360 0.5461 nan 0.0100 -0.0001
## 380 0.5353 nan 0.0100 -0.0001
## 400 0.5245 nan 0.0100 -0.0000
## 420 0.5149 nan 0.0100 -0.0001
## 440 0.5048 nan 0.0100 -0.0000
## 460 0.4960 nan 0.0100 -0.0001
## 480 0.4878 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0038
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2934 nan 0.0100 0.0042
## 4 1.2839 nan 0.0100 0.0041
## 5 1.2746 nan 0.0100 0.0038
## 6 1.2660 nan 0.0100 0.0036
## 7 1.2570 nan 0.0100 0.0042
## 8 1.2488 nan 0.0100 0.0037
## 9 1.2407 nan 0.0100 0.0039
## 10 1.2320 nan 0.0100 0.0039
## 20 1.1566 nan 0.0100 0.0030
## 40 1.0435 nan 0.0100 0.0018
## 60 0.9564 nan 0.0100 0.0017
## 80 0.8892 nan 0.0100 0.0015
## 100 0.8339 nan 0.0100 0.0010
## 120 0.7923 nan 0.0100 0.0008
## 140 0.7572 nan 0.0100 0.0004
## 160 0.7268 nan 0.0100 0.0003
## 180 0.7002 nan 0.0100 0.0004
## 200 0.6775 nan 0.0100 0.0001
## 220 0.6571 nan 0.0100 0.0001
## 240 0.6394 nan 0.0100 0.0001
## 260 0.6235 nan 0.0100 0.0000
## 280 0.6088 nan 0.0100 0.0002
## 300 0.5958 nan 0.0100 0.0002
## 320 0.5827 nan 0.0100 -0.0002
## 340 0.5708 nan 0.0100 -0.0000
## 360 0.5595 nan 0.0100 -0.0000
## 380 0.5491 nan 0.0100 0.0000
## 400 0.5383 nan 0.0100 -0.0001
## 420 0.5286 nan 0.0100 -0.0001
## 440 0.5188 nan 0.0100 -0.0001
## 460 0.5089 nan 0.0100 0.0000
## 480 0.5004 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0041
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2940 nan 0.0100 0.0039
## 4 1.2855 nan 0.0100 0.0041
## 5 1.2774 nan 0.0100 0.0034
## 6 1.2689 nan 0.0100 0.0039
## 7 1.2607 nan 0.0100 0.0037
## 8 1.2528 nan 0.0100 0.0038
## 9 1.2448 nan 0.0100 0.0035
## 10 1.2371 nan 0.0100 0.0036
## 20 1.1661 nan 0.0100 0.0028
## 40 1.0493 nan 0.0100 0.0022
## 60 0.9617 nan 0.0100 0.0018
## 80 0.8919 nan 0.0100 0.0011
## 100 0.8382 nan 0.0100 0.0010
## 120 0.7930 nan 0.0100 0.0006
## 140 0.7580 nan 0.0100 0.0003
## 160 0.7287 nan 0.0100 0.0004
## 180 0.7041 nan 0.0100 0.0004
## 200 0.6839 nan 0.0100 0.0002
## 220 0.6638 nan 0.0100 0.0001
## 240 0.6463 nan 0.0100 -0.0001
## 260 0.6305 nan 0.0100 0.0002
## 280 0.6168 nan 0.0100 0.0001
## 300 0.6028 nan 0.0100 0.0002
## 320 0.5908 nan 0.0100 -0.0000
## 340 0.5796 nan 0.0100 -0.0001
## 360 0.5681 nan 0.0100 0.0000
## 380 0.5571 nan 0.0100 -0.0001
## 400 0.5475 nan 0.0100 -0.0000
## 420 0.5377 nan 0.0100 0.0000
## 440 0.5282 nan 0.0100 -0.0000
## 460 0.5181 nan 0.0100 -0.0000
## 480 0.5091 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3102 nan 0.0100 0.0041
## 2 1.3005 nan 0.0100 0.0042
## 3 1.2915 nan 0.0100 0.0040
## 4 1.2823 nan 0.0100 0.0042
## 5 1.2731 nan 0.0100 0.0041
## 6 1.2640 nan 0.0100 0.0041
## 7 1.2544 nan 0.0100 0.0041
## 8 1.2452 nan 0.0100 0.0042
## 9 1.2365 nan 0.0100 0.0040
## 10 1.2281 nan 0.0100 0.0038
## 20 1.1507 nan 0.0100 0.0030
## 40 1.0283 nan 0.0100 0.0020
## 60 0.9383 nan 0.0100 0.0016
## 80 0.8666 nan 0.0100 0.0010
## 100 0.8113 nan 0.0100 0.0007
## 120 0.7646 nan 0.0100 0.0007
## 140 0.7264 nan 0.0100 0.0005
## 160 0.6946 nan 0.0100 0.0004
## 180 0.6647 nan 0.0100 0.0003
## 200 0.6392 nan 0.0100 0.0004
## 220 0.6172 nan 0.0100 0.0002
## 240 0.5980 nan 0.0100 0.0000
## 260 0.5804 nan 0.0100 0.0001
## 280 0.5634 nan 0.0100 0.0001
## 300 0.5477 nan 0.0100 0.0000
## 320 0.5343 nan 0.0100 -0.0000
## 340 0.5206 nan 0.0100 0.0000
## 360 0.5077 nan 0.0100 -0.0000
## 380 0.4947 nan 0.0100 0.0002
## 400 0.4825 nan 0.0100 0.0002
## 420 0.4712 nan 0.0100 0.0002
## 440 0.4610 nan 0.0100 0.0000
## 460 0.4507 nan 0.0100 -0.0001
## 480 0.4414 nan 0.0100 -0.0001
## 500 0.4317 nan 0.0100 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0042
## 2 1.3015 nan 0.0100 0.0044
## 3 1.2912 nan 0.0100 0.0047
## 4 1.2819 nan 0.0100 0.0043
## 5 1.2733 nan 0.0100 0.0041
## 6 1.2645 nan 0.0100 0.0042
## 7 1.2563 nan 0.0100 0.0036
## 8 1.2472 nan 0.0100 0.0039
## 9 1.2394 nan 0.0100 0.0032
## 10 1.2306 nan 0.0100 0.0038
## 20 1.1561 nan 0.0100 0.0032
## 40 1.0346 nan 0.0100 0.0022
## 60 0.9442 nan 0.0100 0.0016
## 80 0.8721 nan 0.0100 0.0013
## 100 0.8163 nan 0.0100 0.0009
## 120 0.7695 nan 0.0100 0.0007
## 140 0.7311 nan 0.0100 0.0005
## 160 0.6998 nan 0.0100 0.0005
## 180 0.6719 nan 0.0100 0.0003
## 200 0.6486 nan 0.0100 0.0002
## 220 0.6277 nan 0.0100 0.0002
## 240 0.6084 nan 0.0100 0.0003
## 260 0.5894 nan 0.0100 0.0001
## 280 0.5719 nan 0.0100 0.0003
## 300 0.5568 nan 0.0100 0.0001
## 320 0.5434 nan 0.0100 0.0000
## 340 0.5303 nan 0.0100 0.0001
## 360 0.5175 nan 0.0100 -0.0000
## 380 0.5054 nan 0.0100 -0.0001
## 400 0.4933 nan 0.0100 -0.0000
## 420 0.4822 nan 0.0100 0.0001
## 440 0.4723 nan 0.0100 0.0001
## 460 0.4628 nan 0.0100 0.0001
## 480 0.4538 nan 0.0100 -0.0002
## 500 0.4440 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3101 nan 0.0100 0.0048
## 2 1.3010 nan 0.0100 0.0042
## 3 1.2920 nan 0.0100 0.0044
## 4 1.2826 nan 0.0100 0.0042
## 5 1.2740 nan 0.0100 0.0037
## 6 1.2652 nan 0.0100 0.0038
## 7 1.2561 nan 0.0100 0.0039
## 8 1.2474 nan 0.0100 0.0039
## 9 1.2395 nan 0.0100 0.0034
## 10 1.2318 nan 0.0100 0.0035
## 20 1.1569 nan 0.0100 0.0031
## 40 1.0378 nan 0.0100 0.0018
## 60 0.9495 nan 0.0100 0.0015
## 80 0.8798 nan 0.0100 0.0014
## 100 0.8245 nan 0.0100 0.0012
## 120 0.7781 nan 0.0100 0.0007
## 140 0.7395 nan 0.0100 0.0005
## 160 0.7082 nan 0.0100 0.0004
## 180 0.6813 nan 0.0100 0.0003
## 200 0.6588 nan 0.0100 0.0003
## 220 0.6371 nan 0.0100 0.0002
## 240 0.6187 nan 0.0100 0.0000
## 260 0.6018 nan 0.0100 -0.0000
## 280 0.5861 nan 0.0100 -0.0000
## 300 0.5714 nan 0.0100 0.0002
## 320 0.5580 nan 0.0100 0.0001
## 340 0.5453 nan 0.0100 -0.0000
## 360 0.5342 nan 0.0100 -0.0001
## 380 0.5230 nan 0.0100 -0.0001
## 400 0.5118 nan 0.0100 -0.0001
## 420 0.5021 nan 0.0100 0.0000
## 440 0.4924 nan 0.0100 -0.0001
## 460 0.4824 nan 0.0100 -0.0001
## 480 0.4731 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2360 nan 0.1000 0.0402
## 2 1.1683 nan 0.1000 0.0305
## 3 1.1064 nan 0.1000 0.0307
## 4 1.0539 nan 0.1000 0.0215
## 5 1.0074 nan 0.1000 0.0200
## 6 0.9648 nan 0.1000 0.0186
## 7 0.9310 nan 0.1000 0.0101
## 8 0.8990 nan 0.1000 0.0128
## 9 0.8709 nan 0.1000 0.0105
## 10 0.8478 nan 0.1000 0.0081
## 20 0.6969 nan 0.1000 0.0007
## 40 0.5698 nan 0.1000 -0.0004
## 60 0.4949 nan 0.1000 -0.0005
## 80 0.4359 nan 0.1000 -0.0014
## 100 0.3805 nan 0.1000 -0.0002
## 120 0.3358 nan 0.1000 -0.0009
## 140 0.2991 nan 0.1000 -0.0004
## 160 0.2675 nan 0.1000 -0.0006
## 180 0.2436 nan 0.1000 -0.0001
## 200 0.2218 nan 0.1000 -0.0008
## 220 0.2019 nan 0.1000 -0.0002
## 240 0.1830 nan 0.1000 -0.0007
## 260 0.1668 nan 0.1000 -0.0003
## 280 0.1520 nan 0.1000 -0.0003
## 300 0.1393 nan 0.1000 -0.0002
## 320 0.1279 nan 0.1000 0.0001
## 340 0.1172 nan 0.1000 -0.0002
## 360 0.1090 nan 0.1000 -0.0003
## 380 0.1004 nan 0.1000 -0.0003
## 400 0.0925 nan 0.1000 -0.0005
## 420 0.0854 nan 0.1000 -0.0001
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## 460 0.0719 nan 0.1000 -0.0001
## 480 0.0666 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2452 nan 0.1000 0.0344
## 2 1.1714 nan 0.1000 0.0347
## 3 1.1087 nan 0.1000 0.0291
## 4 1.0550 nan 0.1000 0.0196
## 5 1.0128 nan 0.1000 0.0177
## 6 0.9734 nan 0.1000 0.0161
## 7 0.9379 nan 0.1000 0.0167
## 8 0.9016 nan 0.1000 0.0128
## 9 0.8754 nan 0.1000 0.0115
## 10 0.8528 nan 0.1000 0.0087
## 20 0.7109 nan 0.1000 0.0021
## 40 0.5813 nan 0.1000 -0.0007
## 60 0.5016 nan 0.1000 -0.0004
## 80 0.4404 nan 0.1000 -0.0020
## 100 0.3875 nan 0.1000 -0.0006
## 120 0.3450 nan 0.1000 -0.0015
## 140 0.3048 nan 0.1000 -0.0002
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## 180 0.2487 nan 0.1000 -0.0018
## 200 0.2238 nan 0.1000 -0.0008
## 220 0.2045 nan 0.1000 -0.0004
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## 320 0.1309 nan 0.1000 -0.0000
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## 360 0.1118 nan 0.1000 -0.0003
## 380 0.1042 nan 0.1000 -0.0001
## 400 0.0950 nan 0.1000 -0.0002
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## 460 0.0754 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2348 nan 0.1000 0.0428
## 2 1.1717 nan 0.1000 0.0274
## 3 1.1124 nan 0.1000 0.0236
## 4 1.0579 nan 0.1000 0.0236
## 5 1.0154 nan 0.1000 0.0185
## 6 0.9738 nan 0.1000 0.0169
## 7 0.9417 nan 0.1000 0.0107
## 8 0.9123 nan 0.1000 0.0134
## 9 0.8864 nan 0.1000 0.0087
## 10 0.8629 nan 0.1000 0.0087
## 20 0.7096 nan 0.1000 0.0013
## 40 0.5977 nan 0.1000 0.0009
## 60 0.5186 nan 0.1000 -0.0008
## 80 0.4521 nan 0.1000 -0.0000
## 100 0.3970 nan 0.1000 0.0006
## 120 0.3647 nan 0.1000 -0.0007
## 140 0.3262 nan 0.1000 -0.0002
## 160 0.2954 nan 0.1000 -0.0003
## 180 0.2703 nan 0.1000 -0.0012
## 200 0.2484 nan 0.1000 -0.0007
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## 320 0.1478 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2311 nan 0.1000 0.0346
## 2 1.1573 nan 0.1000 0.0330
## 3 1.0909 nan 0.1000 0.0297
## 4 1.0416 nan 0.1000 0.0221
## 5 0.9951 nan 0.1000 0.0192
## 6 0.9564 nan 0.1000 0.0166
## 7 0.9235 nan 0.1000 0.0141
## 8 0.8902 nan 0.1000 0.0141
## 9 0.8615 nan 0.1000 0.0109
## 10 0.8372 nan 0.1000 0.0093
## 20 0.6817 nan 0.1000 0.0005
## 40 0.5316 nan 0.1000 -0.0006
## 60 0.4421 nan 0.1000 -0.0015
## 80 0.3796 nan 0.1000 -0.0002
## 100 0.3264 nan 0.1000 -0.0002
## 120 0.2844 nan 0.1000 -0.0003
## 140 0.2426 nan 0.1000 -0.0008
## 160 0.2118 nan 0.1000 -0.0003
## 180 0.1916 nan 0.1000 0.0001
## 200 0.1707 nan 0.1000 -0.0003
## 220 0.1524 nan 0.1000 -0.0007
## 240 0.1344 nan 0.1000 -0.0004
## 260 0.1204 nan 0.1000 -0.0007
## 280 0.1065 nan 0.1000 -0.0001
## 300 0.0958 nan 0.1000 -0.0001
## 320 0.0862 nan 0.1000 -0.0002
## 340 0.0778 nan 0.1000 -0.0002
## 360 0.0700 nan 0.1000 -0.0003
## 380 0.0637 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 -0.0001
## 420 0.0524 nan 0.1000 -0.0000
## 440 0.0481 nan 0.1000 -0.0002
## 460 0.0431 nan 0.1000 -0.0000
## 480 0.0388 nan 0.1000 -0.0000
## 500 0.0355 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0405
## 2 1.1639 nan 0.1000 0.0324
## 3 1.1015 nan 0.1000 0.0281
## 4 1.0484 nan 0.1000 0.0210
## 5 1.0029 nan 0.1000 0.0201
## 6 0.9600 nan 0.1000 0.0159
## 7 0.9249 nan 0.1000 0.0154
## 8 0.8939 nan 0.1000 0.0134
## 9 0.8645 nan 0.1000 0.0122
## 10 0.8407 nan 0.1000 0.0108
## 20 0.6863 nan 0.1000 0.0041
## 40 0.5444 nan 0.1000 -0.0001
## 60 0.4535 nan 0.1000 -0.0010
## 80 0.3910 nan 0.1000 -0.0014
## 100 0.3413 nan 0.1000 -0.0005
## 120 0.2966 nan 0.1000 -0.0005
## 140 0.2612 nan 0.1000 -0.0007
## 160 0.2282 nan 0.1000 -0.0004
## 180 0.1996 nan 0.1000 -0.0006
## 200 0.1745 nan 0.1000 -0.0005
## 220 0.1546 nan 0.1000 -0.0006
## 240 0.1381 nan 0.1000 -0.0005
## 260 0.1232 nan 0.1000 -0.0001
## 280 0.1102 nan 0.1000 -0.0005
## 300 0.0978 nan 0.1000 0.0001
## 320 0.0879 nan 0.1000 -0.0002
## 340 0.0801 nan 0.1000 -0.0003
## 360 0.0724 nan 0.1000 -0.0003
## 380 0.0654 nan 0.1000 -0.0002
## 400 0.0589 nan 0.1000 -0.0002
## 420 0.0532 nan 0.1000 -0.0001
## 440 0.0486 nan 0.1000 -0.0003
## 460 0.0437 nan 0.1000 0.0000
## 480 0.0403 nan 0.1000 -0.0001
## 500 0.0364 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2275 nan 0.1000 0.0377
## 2 1.1582 nan 0.1000 0.0295
## 3 1.0990 nan 0.1000 0.0238
## 4 1.0547 nan 0.1000 0.0191
## 5 1.0019 nan 0.1000 0.0208
## 6 0.9658 nan 0.1000 0.0114
## 7 0.9262 nan 0.1000 0.0150
## 8 0.8923 nan 0.1000 0.0145
## 9 0.8662 nan 0.1000 0.0099
## 10 0.8400 nan 0.1000 0.0075
## 20 0.6911 nan 0.1000 0.0019
## 40 0.5566 nan 0.1000 -0.0012
## 60 0.4731 nan 0.1000 -0.0001
## 80 0.4049 nan 0.1000 -0.0008
## 100 0.3464 nan 0.1000 -0.0011
## 120 0.3010 nan 0.1000 -0.0011
## 140 0.2658 nan 0.1000 -0.0011
## 160 0.2350 nan 0.1000 -0.0005
## 180 0.2068 nan 0.1000 -0.0012
## 200 0.1848 nan 0.1000 -0.0012
## 220 0.1654 nan 0.1000 -0.0010
## 240 0.1479 nan 0.1000 -0.0007
## 260 0.1341 nan 0.1000 -0.0006
## 280 0.1201 nan 0.1000 -0.0006
## 300 0.1079 nan 0.1000 -0.0005
## 320 0.0977 nan 0.1000 -0.0003
## 340 0.0880 nan 0.1000 -0.0002
## 360 0.0803 nan 0.1000 -0.0005
## 380 0.0726 nan 0.1000 -0.0003
## 400 0.0662 nan 0.1000 -0.0004
## 420 0.0607 nan 0.1000 -0.0001
## 440 0.0556 nan 0.1000 -0.0002
## 460 0.0503 nan 0.1000 -0.0002
## 480 0.0463 nan 0.1000 -0.0002
## 500 0.0423 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2289 nan 0.1000 0.0437
## 2 1.1502 nan 0.1000 0.0301
## 3 1.0861 nan 0.1000 0.0285
## 4 1.0256 nan 0.1000 0.0237
## 5 0.9811 nan 0.1000 0.0183
## 6 0.9349 nan 0.1000 0.0198
## 7 0.8934 nan 0.1000 0.0185
## 8 0.8628 nan 0.1000 0.0128
## 9 0.8328 nan 0.1000 0.0098
## 10 0.8031 nan 0.1000 0.0119
## 20 0.6356 nan 0.1000 0.0029
## 40 0.4870 nan 0.1000 0.0005
## 60 0.3925 nan 0.1000 0.0001
## 80 0.3313 nan 0.1000 -0.0003
## 100 0.2798 nan 0.1000 -0.0001
## 120 0.2322 nan 0.1000 0.0006
## 140 0.1996 nan 0.1000 -0.0013
## 160 0.1687 nan 0.1000 -0.0006
## 180 0.1448 nan 0.1000 -0.0001
## 200 0.1269 nan 0.1000 -0.0006
## 220 0.1102 nan 0.1000 -0.0001
## 240 0.0964 nan 0.1000 -0.0003
## 260 0.0843 nan 0.1000 -0.0002
## 280 0.0740 nan 0.1000 -0.0000
## 300 0.0653 nan 0.1000 -0.0002
## 320 0.0575 nan 0.1000 -0.0000
## 340 0.0507 nan 0.1000 -0.0002
## 360 0.0448 nan 0.1000 -0.0001
## 380 0.0397 nan 0.1000 -0.0001
## 400 0.0353 nan 0.1000 -0.0001
## 420 0.0312 nan 0.1000 -0.0001
## 440 0.0278 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0000
## 480 0.0215 nan 0.1000 -0.0000
## 500 0.0191 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2175 nan 0.1000 0.0444
## 2 1.1461 nan 0.1000 0.0305
## 3 1.0787 nan 0.1000 0.0294
## 4 1.0245 nan 0.1000 0.0236
## 5 0.9767 nan 0.1000 0.0211
## 6 0.9341 nan 0.1000 0.0172
## 7 0.8926 nan 0.1000 0.0159
## 8 0.8619 nan 0.1000 0.0126
## 9 0.8333 nan 0.1000 0.0131
## 10 0.8083 nan 0.1000 0.0090
## 20 0.6507 nan 0.1000 0.0016
## 40 0.4908 nan 0.1000 -0.0004
## 60 0.4022 nan 0.1000 -0.0001
## 80 0.3316 nan 0.1000 -0.0000
## 100 0.2780 nan 0.1000 -0.0005
## 120 0.2377 nan 0.1000 -0.0003
## 140 0.2037 nan 0.1000 -0.0002
## 160 0.1751 nan 0.1000 -0.0008
## 180 0.1513 nan 0.1000 -0.0008
## 200 0.1304 nan 0.1000 -0.0004
## 220 0.1127 nan 0.1000 -0.0003
## 240 0.0980 nan 0.1000 -0.0001
## 260 0.0864 nan 0.1000 -0.0001
## 280 0.0754 nan 0.1000 -0.0003
## 300 0.0651 nan 0.1000 -0.0002
## 320 0.0576 nan 0.1000 -0.0002
## 340 0.0516 nan 0.1000 -0.0001
## 360 0.0453 nan 0.1000 -0.0002
## 380 0.0404 nan 0.1000 -0.0002
## 400 0.0360 nan 0.1000 -0.0002
## 420 0.0317 nan 0.1000 -0.0002
## 440 0.0283 nan 0.1000 -0.0001
## 460 0.0252 nan 0.1000 -0.0000
## 480 0.0223 nan 0.1000 -0.0000
## 500 0.0199 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.1000 0.0361
## 2 1.1628 nan 0.1000 0.0325
## 3 1.0930 nan 0.1000 0.0302
## 4 1.0431 nan 0.1000 0.0223
## 5 0.9887 nan 0.1000 0.0215
## 6 0.9467 nan 0.1000 0.0164
## 7 0.9116 nan 0.1000 0.0141
## 8 0.8762 nan 0.1000 0.0143
## 9 0.8489 nan 0.1000 0.0124
## 10 0.8211 nan 0.1000 0.0098
## 20 0.6559 nan 0.1000 0.0023
## 40 0.5206 nan 0.1000 -0.0008
## 60 0.4305 nan 0.1000 -0.0007
## 80 0.3595 nan 0.1000 -0.0017
## 100 0.3039 nan 0.1000 -0.0004
## 120 0.2624 nan 0.1000 -0.0014
## 140 0.2222 nan 0.1000 -0.0007
## 160 0.1925 nan 0.1000 -0.0005
## 180 0.1675 nan 0.1000 -0.0010
## 200 0.1462 nan 0.1000 -0.0004
## 220 0.1265 nan 0.1000 -0.0000
## 240 0.1107 nan 0.1000 -0.0005
## 260 0.0966 nan 0.1000 -0.0004
## 280 0.0851 nan 0.1000 -0.0004
## 300 0.0743 nan 0.1000 -0.0002
## 320 0.0654 nan 0.1000 -0.0002
## 340 0.0582 nan 0.1000 -0.0003
## 360 0.0516 nan 0.1000 -0.0002
## 380 0.0456 nan 0.1000 -0.0002
## 400 0.0409 nan 0.1000 -0.0002
## 420 0.0366 nan 0.1000 -0.0002
## 440 0.0329 nan 0.1000 -0.0001
## 460 0.0297 nan 0.1000 -0.0002
## 480 0.0267 nan 0.1000 -0.0001
## 500 0.0240 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3153 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2883 nan 0.0010 0.0003
## 60 1.2726 nan 0.0010 0.0003
## 80 1.2579 nan 0.0010 0.0003
## 100 1.2435 nan 0.0010 0.0003
## 120 1.2295 nan 0.0010 0.0003
## 140 1.2160 nan 0.0010 0.0003
## 160 1.2028 nan 0.0010 0.0003
## 180 1.1899 nan 0.0010 0.0003
## 200 1.1776 nan 0.0010 0.0003
## 220 1.1658 nan 0.0010 0.0003
## 240 1.1540 nan 0.0010 0.0002
## 260 1.1427 nan 0.0010 0.0002
## 280 1.1320 nan 0.0010 0.0002
## 300 1.1212 nan 0.0010 0.0002
## 320 1.1107 nan 0.0010 0.0002
## 340 1.1008 nan 0.0010 0.0002
## 360 1.0911 nan 0.0010 0.0002
## 380 1.0815 nan 0.0010 0.0002
## 400 1.0724 nan 0.0010 0.0002
## 420 1.0633 nan 0.0010 0.0002
## 440 1.0545 nan 0.0010 0.0002
## 460 1.0458 nan 0.0010 0.0002
## 480 1.0374 nan 0.0010 0.0002
## 500 1.0292 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0003
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3045 nan 0.0010 0.0004
## 40 1.2885 nan 0.0010 0.0003
## 60 1.2731 nan 0.0010 0.0003
## 80 1.2581 nan 0.0010 0.0003
## 100 1.2437 nan 0.0010 0.0003
## 120 1.2298 nan 0.0010 0.0003
## 140 1.2165 nan 0.0010 0.0003
## 160 1.2033 nan 0.0010 0.0003
## 180 1.1906 nan 0.0010 0.0002
## 200 1.1781 nan 0.0010 0.0003
## 220 1.1662 nan 0.0010 0.0003
## 240 1.1545 nan 0.0010 0.0002
## 260 1.1432 nan 0.0010 0.0003
## 280 1.1323 nan 0.0010 0.0002
## 300 1.1216 nan 0.0010 0.0002
## 320 1.1115 nan 0.0010 0.0002
## 340 1.1014 nan 0.0010 0.0002
## 360 1.0919 nan 0.0010 0.0002
## 380 1.0822 nan 0.0010 0.0002
## 400 1.0729 nan 0.0010 0.0002
## 420 1.0638 nan 0.0010 0.0002
## 440 1.0549 nan 0.0010 0.0002
## 460 1.0466 nan 0.0010 0.0002
## 480 1.0384 nan 0.0010 0.0002
## 500 1.0303 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3162 nan 0.0010 0.0004
## 7 1.3154 nan 0.0010 0.0004
## 8 1.3146 nan 0.0010 0.0004
## 9 1.3138 nan 0.0010 0.0004
## 10 1.3130 nan 0.0010 0.0004
## 20 1.3047 nan 0.0010 0.0004
## 40 1.2890 nan 0.0010 0.0004
## 60 1.2737 nan 0.0010 0.0004
## 80 1.2587 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2169 nan 0.0010 0.0003
## 160 1.2038 nan 0.0010 0.0003
## 180 1.1912 nan 0.0010 0.0003
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1671 nan 0.0010 0.0003
## 240 1.1554 nan 0.0010 0.0003
## 260 1.1442 nan 0.0010 0.0002
## 280 1.1330 nan 0.0010 0.0003
## 300 1.1225 nan 0.0010 0.0002
## 320 1.1124 nan 0.0010 0.0002
## 340 1.1024 nan 0.0010 0.0002
## 360 1.0927 nan 0.0010 0.0002
## 380 1.0833 nan 0.0010 0.0002
## 400 1.0740 nan 0.0010 0.0002
## 420 1.0652 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0479 nan 0.0010 0.0002
## 480 1.0397 nan 0.0010 0.0001
## 500 1.0316 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2862 nan 0.0010 0.0003
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2533 nan 0.0010 0.0004
## 100 1.2378 nan 0.0010 0.0003
## 120 1.2228 nan 0.0010 0.0004
## 140 1.2088 nan 0.0010 0.0003
## 160 1.1950 nan 0.0010 0.0003
## 180 1.1815 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1555 nan 0.0010 0.0002
## 240 1.1433 nan 0.0010 0.0003
## 260 1.1314 nan 0.0010 0.0003
## 280 1.1199 nan 0.0010 0.0002
## 300 1.1087 nan 0.0010 0.0002
## 320 1.0977 nan 0.0010 0.0002
## 340 1.0869 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0665 nan 0.0010 0.0002
## 400 1.0566 nan 0.0010 0.0002
## 420 1.0473 nan 0.0010 0.0002
## 440 1.0381 nan 0.0010 0.0002
## 460 1.0289 nan 0.0010 0.0002
## 480 1.0204 nan 0.0010 0.0001
## 500 1.0118 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0005
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3133 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0003
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2547 nan 0.0010 0.0004
## 100 1.2394 nan 0.0010 0.0003
## 120 1.2244 nan 0.0010 0.0003
## 140 1.2098 nan 0.0010 0.0003
## 160 1.1955 nan 0.0010 0.0003
## 180 1.1822 nan 0.0010 0.0003
## 200 1.1693 nan 0.0010 0.0003
## 220 1.1569 nan 0.0010 0.0003
## 240 1.1445 nan 0.0010 0.0002
## 260 1.1328 nan 0.0010 0.0002
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1102 nan 0.0010 0.0003
## 320 1.0993 nan 0.0010 0.0003
## 340 1.0887 nan 0.0010 0.0002
## 360 1.0784 nan 0.0010 0.0002
## 380 1.0685 nan 0.0010 0.0002
## 400 1.0589 nan 0.0010 0.0002
## 420 1.0494 nan 0.0010 0.0002
## 440 1.0403 nan 0.0010 0.0002
## 460 1.0312 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0139 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3039 nan 0.0010 0.0003
## 40 1.2868 nan 0.0010 0.0003
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2391 nan 0.0010 0.0004
## 120 1.2242 nan 0.0010 0.0003
## 140 1.2104 nan 0.0010 0.0003
## 160 1.1967 nan 0.0010 0.0003
## 180 1.1833 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1574 nan 0.0010 0.0003
## 240 1.1453 nan 0.0010 0.0003
## 260 1.1334 nan 0.0010 0.0003
## 280 1.1220 nan 0.0010 0.0002
## 300 1.1108 nan 0.0010 0.0002
## 320 1.1003 nan 0.0010 0.0002
## 340 1.0897 nan 0.0010 0.0002
## 360 1.0795 nan 0.0010 0.0002
## 380 1.0696 nan 0.0010 0.0002
## 400 1.0600 nan 0.0010 0.0001
## 420 1.0505 nan 0.0010 0.0002
## 440 1.0418 nan 0.0010 0.0002
## 460 1.0329 nan 0.0010 0.0002
## 480 1.0242 nan 0.0010 0.0002
## 500 1.0157 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3027 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2346 nan 0.0010 0.0003
## 120 1.2188 nan 0.0010 0.0004
## 140 1.2040 nan 0.0010 0.0003
## 160 1.1896 nan 0.0010 0.0003
## 180 1.1754 nan 0.0010 0.0003
## 200 1.1620 nan 0.0010 0.0003
## 220 1.1487 nan 0.0010 0.0003
## 240 1.1357 nan 0.0010 0.0003
## 260 1.1232 nan 0.0010 0.0003
## 280 1.1109 nan 0.0010 0.0003
## 300 1.0993 nan 0.0010 0.0002
## 320 1.0879 nan 0.0010 0.0002
## 340 1.0768 nan 0.0010 0.0002
## 360 1.0660 nan 0.0010 0.0002
## 380 1.0553 nan 0.0010 0.0002
## 400 1.0454 nan 0.0010 0.0002
## 420 1.0351 nan 0.0010 0.0002
## 440 1.0257 nan 0.0010 0.0002
## 460 1.0164 nan 0.0010 0.0002
## 480 1.0071 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0005
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2850 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0003
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2057 nan 0.0010 0.0003
## 160 1.1912 nan 0.0010 0.0003
## 180 1.1773 nan 0.0010 0.0002
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## 320 1.0907 nan 0.0010 0.0002
## 340 1.0796 nan 0.0010 0.0002
## 360 1.0688 nan 0.0010 0.0002
## 380 1.0582 nan 0.0010 0.0002
## 400 1.0480 nan 0.0010 0.0002
## 420 1.0380 nan 0.0010 0.0002
## 440 1.0288 nan 0.0010 0.0001
## 460 1.0195 nan 0.0010 0.0002
## 480 1.0103 nan 0.0010 0.0002
## 500 1.0014 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2689 nan 0.0010 0.0004
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## 100 1.2366 nan 0.0010 0.0004
## 120 1.2217 nan 0.0010 0.0003
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## 260 1.1276 nan 0.0010 0.0002
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## 360 1.0716 nan 0.0010 0.0002
## 380 1.0612 nan 0.0010 0.0002
## 400 1.0511 nan 0.0010 0.0002
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## 480 1.0135 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0041
## 2 1.3038 nan 0.0100 0.0041
## 3 1.2962 nan 0.0100 0.0037
## 4 1.2886 nan 0.0100 0.0038
## 5 1.2808 nan 0.0100 0.0035
## 6 1.2729 nan 0.0100 0.0033
## 7 1.2654 nan 0.0100 0.0036
## 8 1.2583 nan 0.0100 0.0031
## 9 1.2515 nan 0.0100 0.0032
## 10 1.2439 nan 0.0100 0.0030
## 20 1.1763 nan 0.0100 0.0027
## 40 1.0718 nan 0.0100 0.0018
## 60 0.9913 nan 0.0100 0.0016
## 80 0.9285 nan 0.0100 0.0011
## 100 0.8780 nan 0.0100 0.0008
## 120 0.8357 nan 0.0100 0.0006
## 140 0.8009 nan 0.0100 0.0007
## 160 0.7715 nan 0.0100 0.0004
## 180 0.7453 nan 0.0100 0.0000
## 200 0.7212 nan 0.0100 0.0002
## 220 0.7009 nan 0.0100 0.0003
## 240 0.6842 nan 0.0100 0.0000
## 260 0.6690 nan 0.0100 0.0001
## 280 0.6551 nan 0.0100 -0.0000
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## 340 0.6169 nan 0.0100 0.0002
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## 380 0.5947 nan 0.0100 -0.0000
## 400 0.5846 nan 0.0100 -0.0001
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## 460 0.5596 nan 0.0100 0.0001
## 480 0.5514 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0041
## 2 1.3043 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0042
## 4 1.2882 nan 0.0100 0.0031
## 5 1.2804 nan 0.0100 0.0037
## 6 1.2726 nan 0.0100 0.0035
## 7 1.2653 nan 0.0100 0.0031
## 8 1.2584 nan 0.0100 0.0031
## 9 1.2505 nan 0.0100 0.0037
## 10 1.2425 nan 0.0100 0.0037
## 20 1.1771 nan 0.0100 0.0025
## 40 1.0711 nan 0.0100 0.0020
## 60 0.9923 nan 0.0100 0.0016
## 80 0.9283 nan 0.0100 0.0011
## 100 0.8784 nan 0.0100 0.0009
## 120 0.8380 nan 0.0100 0.0007
## 140 0.8025 nan 0.0100 0.0006
## 160 0.7744 nan 0.0100 0.0005
## 180 0.7507 nan 0.0100 0.0002
## 200 0.7281 nan 0.0100 0.0004
## 220 0.7092 nan 0.0100 0.0003
## 240 0.6921 nan 0.0100 -0.0001
## 260 0.6761 nan 0.0100 0.0001
## 280 0.6629 nan 0.0100 0.0001
## 300 0.6511 nan 0.0100 -0.0001
## 320 0.6389 nan 0.0100 0.0002
## 340 0.6284 nan 0.0100 0.0000
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## 380 0.6074 nan 0.0100 0.0000
## 400 0.5973 nan 0.0100 -0.0001
## 420 0.5882 nan 0.0100 0.0001
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## 460 0.5701 nan 0.0100 -0.0002
## 480 0.5614 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0038
## 2 1.3044 nan 0.0100 0.0041
## 3 1.2966 nan 0.0100 0.0037
## 4 1.2888 nan 0.0100 0.0036
## 5 1.2811 nan 0.0100 0.0039
## 6 1.2742 nan 0.0100 0.0033
## 7 1.2660 nan 0.0100 0.0034
## 8 1.2586 nan 0.0100 0.0035
## 9 1.2512 nan 0.0100 0.0032
## 10 1.2438 nan 0.0100 0.0035
## 20 1.1781 nan 0.0100 0.0029
## 40 1.0728 nan 0.0100 0.0020
## 60 0.9935 nan 0.0100 0.0013
## 80 0.9303 nan 0.0100 0.0010
## 100 0.8809 nan 0.0100 0.0007
## 120 0.8390 nan 0.0100 0.0006
## 140 0.8059 nan 0.0100 0.0004
## 160 0.7776 nan 0.0100 0.0003
## 180 0.7527 nan 0.0100 0.0004
## 200 0.7325 nan 0.0100 0.0000
## 220 0.7147 nan 0.0100 -0.0000
## 240 0.6977 nan 0.0100 0.0002
## 260 0.6821 nan 0.0100 0.0002
## 280 0.6685 nan 0.0100 -0.0000
## 300 0.6551 nan 0.0100 0.0001
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## 340 0.6313 nan 0.0100 0.0001
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## 380 0.6099 nan 0.0100 -0.0000
## 400 0.6006 nan 0.0100 -0.0000
## 420 0.5904 nan 0.0100 -0.0000
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## 460 0.5724 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0043
## 2 1.3030 nan 0.0100 0.0040
## 3 1.2947 nan 0.0100 0.0040
## 4 1.2858 nan 0.0100 0.0040
## 5 1.2779 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0032
## 7 1.2608 nan 0.0100 0.0037
## 8 1.2532 nan 0.0100 0.0032
## 9 1.2452 nan 0.0100 0.0040
## 10 1.2376 nan 0.0100 0.0032
## 20 1.1686 nan 0.0100 0.0026
## 40 1.0588 nan 0.0100 0.0022
## 60 0.9743 nan 0.0100 0.0015
## 80 0.9081 nan 0.0100 0.0011
## 100 0.8547 nan 0.0100 0.0008
## 120 0.8125 nan 0.0100 0.0005
## 140 0.7767 nan 0.0100 0.0005
## 160 0.7465 nan 0.0100 0.0005
## 180 0.7190 nan 0.0100 0.0003
## 200 0.6944 nan 0.0100 0.0002
## 220 0.6737 nan 0.0100 0.0000
## 240 0.6536 nan 0.0100 0.0002
## 260 0.6349 nan 0.0100 0.0001
## 280 0.6188 nan 0.0100 0.0001
## 300 0.6038 nan 0.0100 -0.0000
## 320 0.5890 nan 0.0100 0.0001
## 340 0.5763 nan 0.0100 0.0001
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## 380 0.5514 nan 0.0100 -0.0000
## 400 0.5397 nan 0.0100 0.0002
## 420 0.5289 nan 0.0100 -0.0000
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## 480 0.4999 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0043
## 2 1.3025 nan 0.0100 0.0034
## 3 1.2936 nan 0.0100 0.0039
## 4 1.2851 nan 0.0100 0.0038
## 5 1.2774 nan 0.0100 0.0037
## 6 1.2693 nan 0.0100 0.0034
## 7 1.2623 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0036
## 9 1.2467 nan 0.0100 0.0028
## 10 1.2392 nan 0.0100 0.0035
## 20 1.1690 nan 0.0100 0.0024
## 40 1.0565 nan 0.0100 0.0022
## 60 0.9728 nan 0.0100 0.0017
## 80 0.9069 nan 0.0100 0.0012
## 100 0.8544 nan 0.0100 0.0011
## 120 0.8105 nan 0.0100 0.0008
## 140 0.7735 nan 0.0100 0.0006
## 160 0.7444 nan 0.0100 0.0003
## 180 0.7180 nan 0.0100 0.0004
## 200 0.6958 nan 0.0100 0.0001
## 220 0.6746 nan 0.0100 0.0004
## 240 0.6551 nan 0.0100 0.0002
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## 280 0.6207 nan 0.0100 0.0001
## 300 0.6065 nan 0.0100 0.0001
## 320 0.5940 nan 0.0100 0.0000
## 340 0.5823 nan 0.0100 -0.0001
## 360 0.5699 nan 0.0100 0.0000
## 380 0.5584 nan 0.0100 -0.0003
## 400 0.5466 nan 0.0100 -0.0000
## 420 0.5361 nan 0.0100 -0.0001
## 440 0.5250 nan 0.0100 0.0001
## 460 0.5156 nan 0.0100 -0.0002
## 480 0.5059 nan 0.0100 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0038
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2956 nan 0.0100 0.0038
## 4 1.2872 nan 0.0100 0.0041
## 5 1.2791 nan 0.0100 0.0032
## 6 1.2708 nan 0.0100 0.0041
## 7 1.2630 nan 0.0100 0.0037
## 8 1.2552 nan 0.0100 0.0036
## 9 1.2474 nan 0.0100 0.0035
## 10 1.2402 nan 0.0100 0.0033
## 20 1.1727 nan 0.0100 0.0030
## 40 1.0626 nan 0.0100 0.0019
## 60 0.9786 nan 0.0100 0.0016
## 80 0.9124 nan 0.0100 0.0013
## 100 0.8596 nan 0.0100 0.0009
## 120 0.8179 nan 0.0100 0.0006
## 140 0.7824 nan 0.0100 0.0008
## 160 0.7506 nan 0.0100 0.0002
## 180 0.7251 nan 0.0100 0.0003
## 200 0.7030 nan 0.0100 0.0002
## 220 0.6840 nan 0.0100 0.0001
## 240 0.6653 nan 0.0100 -0.0000
## 260 0.6498 nan 0.0100 -0.0001
## 280 0.6339 nan 0.0100 -0.0001
## 300 0.6187 nan 0.0100 0.0001
## 320 0.6049 nan 0.0100 0.0001
## 340 0.5924 nan 0.0100 0.0001
## 360 0.5802 nan 0.0100 -0.0000
## 380 0.5674 nan 0.0100 0.0001
## 400 0.5566 nan 0.0100 -0.0001
## 420 0.5455 nan 0.0100 0.0001
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## 460 0.5257 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3026 nan 0.0100 0.0040
## 3 1.2929 nan 0.0100 0.0039
## 4 1.2840 nan 0.0100 0.0039
## 5 1.2758 nan 0.0100 0.0037
## 6 1.2671 nan 0.0100 0.0039
## 7 1.2582 nan 0.0100 0.0040
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2416 nan 0.0100 0.0040
## 10 1.2337 nan 0.0100 0.0037
## 20 1.1617 nan 0.0100 0.0026
## 40 1.0445 nan 0.0100 0.0021
## 60 0.9548 nan 0.0100 0.0017
## 80 0.8844 nan 0.0100 0.0012
## 100 0.8296 nan 0.0100 0.0009
## 120 0.7843 nan 0.0100 0.0007
## 140 0.7457 nan 0.0100 0.0007
## 160 0.7120 nan 0.0100 0.0002
## 180 0.6846 nan 0.0100 0.0003
## 200 0.6603 nan 0.0100 0.0003
## 220 0.6382 nan 0.0100 0.0003
## 240 0.6174 nan 0.0100 0.0002
## 260 0.5983 nan 0.0100 -0.0001
## 280 0.5816 nan 0.0100 0.0000
## 300 0.5649 nan 0.0100 0.0000
## 320 0.5506 nan 0.0100 0.0002
## 340 0.5360 nan 0.0100 0.0001
## 360 0.5237 nan 0.0100 -0.0002
## 380 0.5117 nan 0.0100 -0.0000
## 400 0.4998 nan 0.0100 0.0001
## 420 0.4877 nan 0.0100 0.0002
## 440 0.4772 nan 0.0100 0.0001
## 460 0.4660 nan 0.0100 -0.0001
## 480 0.4559 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0042
## 2 1.3027 nan 0.0100 0.0042
## 3 1.2935 nan 0.0100 0.0042
## 4 1.2843 nan 0.0100 0.0040
## 5 1.2758 nan 0.0100 0.0038
## 6 1.2676 nan 0.0100 0.0038
## 7 1.2590 nan 0.0100 0.0038
## 8 1.2512 nan 0.0100 0.0035
## 9 1.2435 nan 0.0100 0.0036
## 10 1.2353 nan 0.0100 0.0034
## 20 1.1646 nan 0.0100 0.0029
## 40 1.0499 nan 0.0100 0.0020
## 60 0.9592 nan 0.0100 0.0019
## 80 0.8904 nan 0.0100 0.0011
## 100 0.8343 nan 0.0100 0.0009
## 120 0.7878 nan 0.0100 0.0006
## 140 0.7505 nan 0.0100 0.0004
## 160 0.7169 nan 0.0100 0.0004
## 180 0.6871 nan 0.0100 0.0003
## 200 0.6625 nan 0.0100 0.0002
## 220 0.6410 nan 0.0100 0.0000
## 240 0.6207 nan 0.0100 0.0001
## 260 0.6012 nan 0.0100 0.0001
## 280 0.5838 nan 0.0100 0.0000
## 300 0.5675 nan 0.0100 0.0002
## 320 0.5525 nan 0.0100 0.0000
## 340 0.5373 nan 0.0100 0.0000
## 360 0.5255 nan 0.0100 -0.0000
## 380 0.5129 nan 0.0100 0.0000
## 400 0.5010 nan 0.0100 0.0000
## 420 0.4889 nan 0.0100 -0.0001
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## 460 0.4683 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3029 nan 0.0100 0.0039
## 3 1.2944 nan 0.0100 0.0042
## 4 1.2860 nan 0.0100 0.0038
## 5 1.2781 nan 0.0100 0.0035
## 6 1.2698 nan 0.0100 0.0038
## 7 1.2618 nan 0.0100 0.0033
## 8 1.2531 nan 0.0100 0.0038
## 9 1.2456 nan 0.0100 0.0035
## 10 1.2373 nan 0.0100 0.0037
## 20 1.1674 nan 0.0100 0.0030
## 40 1.0538 nan 0.0100 0.0023
## 60 0.9659 nan 0.0100 0.0013
## 80 0.8987 nan 0.0100 0.0011
## 100 0.8445 nan 0.0100 0.0009
## 120 0.7980 nan 0.0100 0.0007
## 140 0.7615 nan 0.0100 0.0005
## 160 0.7286 nan 0.0100 0.0003
## 180 0.7021 nan 0.0100 0.0002
## 200 0.6769 nan 0.0100 0.0002
## 220 0.6554 nan 0.0100 0.0002
## 240 0.6359 nan 0.0100 -0.0000
## 260 0.6197 nan 0.0100 -0.0000
## 280 0.6027 nan 0.0100 0.0000
## 300 0.5876 nan 0.0100 -0.0000
## 320 0.5724 nan 0.0100 0.0001
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## 360 0.5443 nan 0.0100 -0.0001
## 380 0.5325 nan 0.0100 -0.0001
## 400 0.5207 nan 0.0100 -0.0002
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## 460 0.4871 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0323
## 2 1.1750 nan 0.1000 0.0320
## 3 1.1131 nan 0.1000 0.0266
## 4 1.0713 nan 0.1000 0.0193
## 5 1.0296 nan 0.1000 0.0192
## 6 0.9890 nan 0.1000 0.0195
## 7 0.9552 nan 0.1000 0.0153
## 8 0.9293 nan 0.1000 0.0092
## 9 0.9025 nan 0.1000 0.0100
## 10 0.8796 nan 0.1000 0.0091
## 20 0.7259 nan 0.1000 0.0023
## 40 0.5947 nan 0.1000 -0.0019
## 60 0.5178 nan 0.1000 0.0001
## 80 0.4572 nan 0.1000 -0.0007
## 100 0.3960 nan 0.1000 -0.0011
## 120 0.3477 nan 0.1000 -0.0003
## 140 0.3100 nan 0.1000 0.0000
## 160 0.2745 nan 0.1000 -0.0004
## 180 0.2487 nan 0.1000 -0.0005
## 200 0.2226 nan 0.1000 -0.0001
## 220 0.2020 nan 0.1000 -0.0006
## 240 0.1840 nan 0.1000 -0.0003
## 260 0.1668 nan 0.1000 -0.0004
## 280 0.1514 nan 0.1000 -0.0009
## 300 0.1379 nan 0.1000 -0.0000
## 320 0.1267 nan 0.1000 -0.0001
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## 360 0.1077 nan 0.1000 -0.0001
## 380 0.0989 nan 0.1000 -0.0004
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## 420 0.0841 nan 0.1000 -0.0003
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## 460 0.0715 nan 0.1000 -0.0002
## 480 0.0662 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2395 nan 0.1000 0.0399
## 2 1.1757 nan 0.1000 0.0288
## 3 1.1171 nan 0.1000 0.0281
## 4 1.0722 nan 0.1000 0.0170
## 5 1.0259 nan 0.1000 0.0180
## 6 0.9897 nan 0.1000 0.0160
## 7 0.9586 nan 0.1000 0.0125
## 8 0.9313 nan 0.1000 0.0105
## 9 0.9054 nan 0.1000 0.0099
## 10 0.8820 nan 0.1000 0.0101
## 20 0.7385 nan 0.1000 0.0043
## 40 0.6000 nan 0.1000 0.0006
## 60 0.5178 nan 0.1000 -0.0007
## 80 0.4477 nan 0.1000 -0.0006
## 100 0.3967 nan 0.1000 -0.0001
## 120 0.3512 nan 0.1000 -0.0007
## 140 0.3130 nan 0.1000 0.0005
## 160 0.2814 nan 0.1000 -0.0004
## 180 0.2520 nan 0.1000 -0.0008
## 200 0.2297 nan 0.1000 -0.0003
## 220 0.2086 nan 0.1000 -0.0004
## 240 0.1879 nan 0.1000 -0.0010
## 260 0.1721 nan 0.1000 -0.0001
## 280 0.1577 nan 0.1000 -0.0006
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## 320 0.1299 nan 0.1000 -0.0005
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## 380 0.1006 nan 0.1000 -0.0003
## 400 0.0925 nan 0.1000 -0.0004
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## 460 0.0735 nan 0.1000 -0.0001
## 480 0.0674 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2435 nan 0.1000 0.0367
## 2 1.1748 nan 0.1000 0.0324
## 3 1.1148 nan 0.1000 0.0269
## 4 1.0647 nan 0.1000 0.0212
## 5 1.0249 nan 0.1000 0.0189
## 6 0.9871 nan 0.1000 0.0142
## 7 0.9521 nan 0.1000 0.0132
## 8 0.9261 nan 0.1000 0.0117
## 9 0.9022 nan 0.1000 0.0087
## 10 0.8790 nan 0.1000 0.0086
## 20 0.7405 nan 0.1000 0.0023
## 40 0.6139 nan 0.1000 0.0003
## 60 0.5259 nan 0.1000 -0.0002
## 80 0.4583 nan 0.1000 -0.0035
## 100 0.4020 nan 0.1000 -0.0013
## 120 0.3589 nan 0.1000 -0.0002
## 140 0.3239 nan 0.1000 -0.0005
## 160 0.2899 nan 0.1000 -0.0003
## 180 0.2637 nan 0.1000 -0.0015
## 200 0.2421 nan 0.1000 -0.0002
## 220 0.2192 nan 0.1000 -0.0005
## 240 0.2013 nan 0.1000 -0.0003
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## 280 0.1700 nan 0.1000 -0.0008
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## 320 0.1421 nan 0.1000 -0.0001
## 340 0.1305 nan 0.1000 -0.0003
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## 380 0.1101 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0411
## 2 1.1648 nan 0.1000 0.0341
## 3 1.1033 nan 0.1000 0.0247
## 4 1.0479 nan 0.1000 0.0253
## 5 1.0049 nan 0.1000 0.0194
## 6 0.9622 nan 0.1000 0.0169
## 7 0.9194 nan 0.1000 0.0167
## 8 0.8908 nan 0.1000 0.0116
## 9 0.8624 nan 0.1000 0.0108
## 10 0.8373 nan 0.1000 0.0097
## 20 0.6821 nan 0.1000 0.0033
## 40 0.5384 nan 0.1000 -0.0002
## 60 0.4549 nan 0.1000 -0.0012
## 80 0.3852 nan 0.1000 -0.0000
## 100 0.3334 nan 0.1000 -0.0009
## 120 0.2869 nan 0.1000 0.0003
## 140 0.2481 nan 0.1000 -0.0005
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## 180 0.1955 nan 0.1000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0384
## 2 1.1680 nan 0.1000 0.0286
## 3 1.1099 nan 0.1000 0.0265
## 4 1.0536 nan 0.1000 0.0247
## 5 1.0125 nan 0.1000 0.0151
## 6 0.9712 nan 0.1000 0.0152
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## 8 0.9120 nan 0.1000 0.0089
## 9 0.8827 nan 0.1000 0.0116
## 10 0.8608 nan 0.1000 0.0068
## 20 0.6968 nan 0.1000 0.0042
## 40 0.5513 nan 0.1000 -0.0010
## 60 0.4513 nan 0.1000 -0.0012
## 80 0.3860 nan 0.1000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0397
## 2 1.1721 nan 0.1000 0.0324
## 3 1.1092 nan 0.1000 0.0276
## 4 1.0638 nan 0.1000 0.0190
## 5 1.0175 nan 0.1000 0.0200
## 6 0.9738 nan 0.1000 0.0183
## 7 0.9409 nan 0.1000 0.0139
## 8 0.9147 nan 0.1000 0.0108
## 9 0.8812 nan 0.1000 0.0120
## 10 0.8610 nan 0.1000 0.0057
## 20 0.7164 nan 0.1000 0.0003
## 40 0.5645 nan 0.1000 0.0010
## 60 0.4733 nan 0.1000 -0.0006
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## 100 0.3473 nan 0.1000 -0.0009
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## 180 0.2020 nan 0.1000 -0.0009
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## 300 0.1056 nan 0.1000 -0.0006
## 320 0.0950 nan 0.1000 -0.0002
## 340 0.0866 nan 0.1000 -0.0001
## 360 0.0776 nan 0.1000 -0.0003
## 380 0.0696 nan 0.1000 -0.0002
## 400 0.0621 nan 0.1000 -0.0001
## 420 0.0566 nan 0.1000 -0.0003
## 440 0.0512 nan 0.1000 -0.0002
## 460 0.0469 nan 0.1000 -0.0001
## 480 0.0428 nan 0.1000 -0.0002
## 500 0.0386 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0413
## 2 1.1617 nan 0.1000 0.0335
## 3 1.0931 nan 0.1000 0.0300
## 4 1.0370 nan 0.1000 0.0244
## 5 0.9901 nan 0.1000 0.0185
## 6 0.9502 nan 0.1000 0.0160
## 7 0.9167 nan 0.1000 0.0121
## 8 0.8878 nan 0.1000 0.0121
## 9 0.8572 nan 0.1000 0.0100
## 10 0.8331 nan 0.1000 0.0091
## 20 0.6635 nan 0.1000 0.0023
## 40 0.4998 nan 0.1000 -0.0000
## 60 0.3983 nan 0.1000 0.0005
## 80 0.3293 nan 0.1000 -0.0002
## 100 0.2756 nan 0.1000 -0.0012
## 120 0.2282 nan 0.1000 0.0004
## 140 0.1934 nan 0.1000 -0.0001
## 160 0.1650 nan 0.1000 -0.0001
## 180 0.1417 nan 0.1000 -0.0004
## 200 0.1230 nan 0.1000 -0.0000
## 220 0.1077 nan 0.1000 -0.0004
## 240 0.0924 nan 0.1000 -0.0001
## 260 0.0798 nan 0.1000 -0.0003
## 280 0.0705 nan 0.1000 -0.0003
## 300 0.0615 nan 0.1000 -0.0001
## 320 0.0538 nan 0.1000 -0.0001
## 340 0.0476 nan 0.1000 -0.0001
## 360 0.0416 nan 0.1000 -0.0000
## 380 0.0371 nan 0.1000 -0.0002
## 400 0.0322 nan 0.1000 -0.0001
## 420 0.0286 nan 0.1000 -0.0001
## 440 0.0251 nan 0.1000 -0.0000
## 460 0.0223 nan 0.1000 -0.0001
## 480 0.0199 nan 0.1000 -0.0001
## 500 0.0177 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0419
## 2 1.1647 nan 0.1000 0.0313
## 3 1.0931 nan 0.1000 0.0303
## 4 1.0383 nan 0.1000 0.0237
## 5 0.9897 nan 0.1000 0.0187
## 6 0.9503 nan 0.1000 0.0172
## 7 0.9139 nan 0.1000 0.0148
## 8 0.8850 nan 0.1000 0.0128
## 9 0.8597 nan 0.1000 0.0100
## 10 0.8364 nan 0.1000 0.0082
## 20 0.6719 nan 0.1000 0.0023
## 40 0.5142 nan 0.1000 -0.0006
## 60 0.4163 nan 0.1000 -0.0017
## 80 0.3377 nan 0.1000 -0.0001
## 100 0.2817 nan 0.1000 -0.0006
## 120 0.2382 nan 0.1000 -0.0005
## 140 0.2049 nan 0.1000 -0.0006
## 160 0.1765 nan 0.1000 -0.0006
## 180 0.1523 nan 0.1000 0.0000
## 200 0.1327 nan 0.1000 -0.0001
## 220 0.1147 nan 0.1000 -0.0004
## 240 0.0991 nan 0.1000 -0.0001
## 260 0.0877 nan 0.1000 -0.0003
## 280 0.0766 nan 0.1000 -0.0003
## 300 0.0675 nan 0.1000 -0.0002
## 320 0.0590 nan 0.1000 -0.0001
## 340 0.0518 nan 0.1000 -0.0001
## 360 0.0453 nan 0.1000 -0.0002
## 380 0.0400 nan 0.1000 -0.0001
## 400 0.0353 nan 0.1000 -0.0001
## 420 0.0310 nan 0.1000 -0.0001
## 440 0.0274 nan 0.1000 -0.0002
## 460 0.0246 nan 0.1000 -0.0001
## 480 0.0216 nan 0.1000 -0.0000
## 500 0.0189 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0409
## 2 1.1600 nan 0.1000 0.0307
## 3 1.1024 nan 0.1000 0.0234
## 4 1.0550 nan 0.1000 0.0209
## 5 1.0126 nan 0.1000 0.0209
## 6 0.9738 nan 0.1000 0.0187
## 7 0.9351 nan 0.1000 0.0138
## 8 0.8949 nan 0.1000 0.0145
## 9 0.8679 nan 0.1000 0.0086
## 10 0.8426 nan 0.1000 0.0082
## 20 0.6752 nan 0.1000 0.0016
## 40 0.5241 nan 0.1000 0.0014
## 60 0.4196 nan 0.1000 -0.0003
## 80 0.3414 nan 0.1000 -0.0000
## 100 0.2917 nan 0.1000 -0.0008
## 120 0.2469 nan 0.1000 -0.0007
## 140 0.2133 nan 0.1000 -0.0006
## 160 0.1847 nan 0.1000 -0.0005
## 180 0.1596 nan 0.1000 -0.0011
## 200 0.1392 nan 0.1000 -0.0003
## 220 0.1205 nan 0.1000 -0.0006
## 240 0.1047 nan 0.1000 -0.0009
## 260 0.0905 nan 0.1000 -0.0003
## 280 0.0793 nan 0.1000 -0.0003
## 300 0.0696 nan 0.1000 -0.0002
## 320 0.0610 nan 0.1000 -0.0000
## 340 0.0539 nan 0.1000 -0.0002
## 360 0.0475 nan 0.1000 -0.0002
## 380 0.0421 nan 0.1000 -0.0001
## 400 0.0370 nan 0.1000 -0.0003
## 420 0.0324 nan 0.1000 -0.0002
## 440 0.0284 nan 0.1000 -0.0001
## 460 0.0255 nan 0.1000 -0.0001
## 480 0.0223 nan 0.1000 -0.0000
## 500 0.0198 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3031 nan 0.0010 0.0003
## 40 1.2861 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2240 nan 0.0010 0.0003
## 140 1.2094 nan 0.0010 0.0003
## 160 1.1958 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1694 nan 0.0010 0.0003
## 220 1.1568 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0003
## 260 1.1322 nan 0.0010 0.0002
## 280 1.1201 nan 0.0010 0.0003
## 300 1.1089 nan 0.0010 0.0002
## 320 1.0980 nan 0.0010 0.0002
## 340 1.0876 nan 0.0010 0.0002
## 360 1.0773 nan 0.0010 0.0002
## 380 1.0673 nan 0.0010 0.0002
## 400 1.0577 nan 0.0010 0.0002
## 420 1.0481 nan 0.0010 0.0002
## 440 1.0392 nan 0.0010 0.0002
## 460 1.0300 nan 0.0010 0.0002
## 480 1.0216 nan 0.0010 0.0002
## 500 1.0130 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0005
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3118 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0003
## 40 1.2859 nan 0.0010 0.0004
## 60 1.2694 nan 0.0010 0.0003
## 80 1.2536 nan 0.0010 0.0003
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2241 nan 0.0010 0.0003
## 140 1.2095 nan 0.0010 0.0003
## 160 1.1954 nan 0.0010 0.0003
## 180 1.1821 nan 0.0010 0.0002
## 200 1.1688 nan 0.0010 0.0003
## 220 1.1564 nan 0.0010 0.0003
## 240 1.1443 nan 0.0010 0.0002
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1211 nan 0.0010 0.0003
## 300 1.1099 nan 0.0010 0.0002
## 320 1.0989 nan 0.0010 0.0002
## 340 1.0883 nan 0.0010 0.0002
## 360 1.0782 nan 0.0010 0.0002
## 380 1.0681 nan 0.0010 0.0002
## 400 1.0587 nan 0.0010 0.0002
## 420 1.0491 nan 0.0010 0.0002
## 440 1.0401 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0139 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0005
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0004
## 80 1.2549 nan 0.0010 0.0003
## 100 1.2400 nan 0.0010 0.0003
## 120 1.2253 nan 0.0010 0.0003
## 140 1.2111 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1835 nan 0.0010 0.0003
## 200 1.1706 nan 0.0010 0.0003
## 220 1.1581 nan 0.0010 0.0003
## 240 1.1460 nan 0.0010 0.0003
## 260 1.1341 nan 0.0010 0.0003
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1116 nan 0.0010 0.0003
## 320 1.1009 nan 0.0010 0.0003
## 340 1.0903 nan 0.0010 0.0002
## 360 1.0799 nan 0.0010 0.0002
## 380 1.0702 nan 0.0010 0.0002
## 400 1.0605 nan 0.0010 0.0002
## 420 1.0509 nan 0.0010 0.0002
## 440 1.0418 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0240 nan 0.0010 0.0002
## 500 1.0155 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0005
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2836 nan 0.0010 0.0004
## 60 1.2663 nan 0.0010 0.0003
## 80 1.2492 nan 0.0010 0.0004
## 100 1.2330 nan 0.0010 0.0003
## 120 1.2173 nan 0.0010 0.0003
## 140 1.2022 nan 0.0010 0.0003
## 160 1.1875 nan 0.0010 0.0003
## 180 1.1732 nan 0.0010 0.0003
## 200 1.1594 nan 0.0010 0.0003
## 220 1.1456 nan 0.0010 0.0003
## 240 1.1328 nan 0.0010 0.0003
## 260 1.1203 nan 0.0010 0.0002
## 280 1.1084 nan 0.0010 0.0003
## 300 1.0965 nan 0.0010 0.0002
## 320 1.0850 nan 0.0010 0.0003
## 340 1.0737 nan 0.0010 0.0002
## 360 1.0628 nan 0.0010 0.0002
## 380 1.0521 nan 0.0010 0.0002
## 400 1.0420 nan 0.0010 0.0002
## 420 1.0320 nan 0.0010 0.0002
## 440 1.0221 nan 0.0010 0.0002
## 460 1.0129 nan 0.0010 0.0002
## 480 1.0038 nan 0.0010 0.0002
## 500 0.9946 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0005
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3021 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2672 nan 0.0010 0.0004
## 80 1.2503 nan 0.0010 0.0003
## 100 1.2339 nan 0.0010 0.0003
## 120 1.2182 nan 0.0010 0.0004
## 140 1.2032 nan 0.0010 0.0003
## 160 1.1885 nan 0.0010 0.0003
## 180 1.1744 nan 0.0010 0.0003
## 200 1.1607 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1218 nan 0.0010 0.0003
## 280 1.1094 nan 0.0010 0.0003
## 300 1.0976 nan 0.0010 0.0003
## 320 1.0860 nan 0.0010 0.0002
## 340 1.0750 nan 0.0010 0.0002
## 360 1.0642 nan 0.0010 0.0003
## 380 1.0539 nan 0.0010 0.0002
## 400 1.0440 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0152 nan 0.0010 0.0002
## 480 1.0061 nan 0.0010 0.0002
## 500 0.9972 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2843 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2514 nan 0.0010 0.0004
## 100 1.2357 nan 0.0010 0.0004
## 120 1.2201 nan 0.0010 0.0003
## 140 1.2050 nan 0.0010 0.0004
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1768 nan 0.0010 0.0003
## 200 1.1634 nan 0.0010 0.0003
## 220 1.1501 nan 0.0010 0.0003
## 240 1.1373 nan 0.0010 0.0003
## 260 1.1247 nan 0.0010 0.0002
## 280 1.1126 nan 0.0010 0.0002
## 300 1.1011 nan 0.0010 0.0003
## 320 1.0896 nan 0.0010 0.0003
## 340 1.0786 nan 0.0010 0.0002
## 360 1.0677 nan 0.0010 0.0003
## 380 1.0571 nan 0.0010 0.0002
## 400 1.0471 nan 0.0010 0.0002
## 420 1.0373 nan 0.0010 0.0002
## 440 1.0276 nan 0.0010 0.0002
## 460 1.0181 nan 0.0010 0.0002
## 480 1.0088 nan 0.0010 0.0002
## 500 0.9999 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0005
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3158 nan 0.0010 0.0005
## 6 1.3148 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0005
## 20 1.3011 nan 0.0010 0.0004
## 40 1.2821 nan 0.0010 0.0004
## 60 1.2638 nan 0.0010 0.0004
## 80 1.2461 nan 0.0010 0.0004
## 100 1.2291 nan 0.0010 0.0003
## 120 1.2129 nan 0.0010 0.0004
## 140 1.1969 nan 0.0010 0.0004
## 160 1.1817 nan 0.0010 0.0003
## 180 1.1669 nan 0.0010 0.0003
## 200 1.1529 nan 0.0010 0.0003
## 220 1.1386 nan 0.0010 0.0003
## 240 1.1252 nan 0.0010 0.0003
## 260 1.1121 nan 0.0010 0.0003
## 280 1.0995 nan 0.0010 0.0002
## 300 1.0873 nan 0.0010 0.0003
## 320 1.0755 nan 0.0010 0.0003
## 340 1.0637 nan 0.0010 0.0003
## 360 1.0520 nan 0.0010 0.0002
## 380 1.0410 nan 0.0010 0.0002
## 400 1.0304 nan 0.0010 0.0002
## 420 1.0200 nan 0.0010 0.0002
## 440 1.0100 nan 0.0010 0.0002
## 460 1.0001 nan 0.0010 0.0002
## 480 0.9909 nan 0.0010 0.0002
## 500 0.9818 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3166 nan 0.0010 0.0005
## 5 1.3156 nan 0.0010 0.0005
## 6 1.3146 nan 0.0010 0.0004
## 7 1.3137 nan 0.0010 0.0004
## 8 1.3126 nan 0.0010 0.0004
## 9 1.3117 nan 0.0010 0.0005
## 10 1.3107 nan 0.0010 0.0004
## 20 1.3010 nan 0.0010 0.0004
## 40 1.2822 nan 0.0010 0.0004
## 60 1.2642 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0004
## 100 1.2302 nan 0.0010 0.0004
## 120 1.2142 nan 0.0010 0.0004
## 140 1.1982 nan 0.0010 0.0004
## 160 1.1828 nan 0.0010 0.0004
## 180 1.1680 nan 0.0010 0.0003
## 200 1.1540 nan 0.0010 0.0003
## 220 1.1403 nan 0.0010 0.0003
## 240 1.1269 nan 0.0010 0.0003
## 260 1.1140 nan 0.0010 0.0003
## 280 1.1015 nan 0.0010 0.0003
## 300 1.0890 nan 0.0010 0.0003
## 320 1.0770 nan 0.0010 0.0003
## 340 1.0653 nan 0.0010 0.0002
## 360 1.0542 nan 0.0010 0.0002
## 380 1.0435 nan 0.0010 0.0002
## 400 1.0327 nan 0.0010 0.0002
## 420 1.0223 nan 0.0010 0.0002
## 440 1.0123 nan 0.0010 0.0002
## 460 1.0027 nan 0.0010 0.0002
## 480 0.9935 nan 0.0010 0.0002
## 500 0.9843 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0005
## 10 1.3108 nan 0.0010 0.0005
## 20 1.3011 nan 0.0010 0.0005
## 40 1.2829 nan 0.0010 0.0004
## 60 1.2654 nan 0.0010 0.0003
## 80 1.2480 nan 0.0010 0.0004
## 100 1.2314 nan 0.0010 0.0004
## 120 1.2153 nan 0.0010 0.0004
## 140 1.1995 nan 0.0010 0.0003
## 160 1.1846 nan 0.0010 0.0004
## 180 1.1699 nan 0.0010 0.0003
## 200 1.1558 nan 0.0010 0.0003
## 220 1.1426 nan 0.0010 0.0003
## 240 1.1294 nan 0.0010 0.0003
## 260 1.1167 nan 0.0010 0.0003
## 280 1.1041 nan 0.0010 0.0003
## 300 1.0917 nan 0.0010 0.0002
## 320 1.0800 nan 0.0010 0.0002
## 340 1.0686 nan 0.0010 0.0002
## 360 1.0574 nan 0.0010 0.0003
## 380 1.0467 nan 0.0010 0.0002
## 400 1.0361 nan 0.0010 0.0002
## 420 1.0262 nan 0.0010 0.0002
## 440 1.0165 nan 0.0010 0.0002
## 460 1.0070 nan 0.0010 0.0002
## 480 0.9976 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0043
## 3 1.2948 nan 0.0100 0.0035
## 4 1.2859 nan 0.0100 0.0042
## 5 1.2767 nan 0.0100 0.0040
## 6 1.2686 nan 0.0100 0.0040
## 7 1.2608 nan 0.0100 0.0038
## 8 1.2528 nan 0.0100 0.0038
## 9 1.2449 nan 0.0100 0.0035
## 10 1.2371 nan 0.0100 0.0036
## 20 1.1694 nan 0.0100 0.0024
## 40 1.0590 nan 0.0100 0.0021
## 60 0.9728 nan 0.0100 0.0016
## 80 0.9090 nan 0.0100 0.0014
## 100 0.8549 nan 0.0100 0.0009
## 120 0.8114 nan 0.0100 0.0006
## 140 0.7757 nan 0.0100 0.0006
## 160 0.7465 nan 0.0100 0.0004
## 180 0.7201 nan 0.0100 0.0004
## 200 0.6977 nan 0.0100 0.0003
## 220 0.6792 nan 0.0100 0.0002
## 240 0.6630 nan 0.0100 0.0001
## 260 0.6483 nan 0.0100 -0.0000
## 280 0.6337 nan 0.0100 -0.0001
## 300 0.6206 nan 0.0100 0.0001
## 320 0.6083 nan 0.0100 0.0002
## 340 0.5984 nan 0.0100 0.0001
## 360 0.5877 nan 0.0100 -0.0000
## 380 0.5763 nan 0.0100 0.0000
## 400 0.5671 nan 0.0100 -0.0000
## 420 0.5579 nan 0.0100 -0.0000
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## 460 0.5416 nan 0.0100 -0.0000
## 480 0.5335 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3108 nan 0.0100 0.0046
## 2 1.3017 nan 0.0100 0.0040
## 3 1.2934 nan 0.0100 0.0038
## 4 1.2854 nan 0.0100 0.0038
## 5 1.2779 nan 0.0100 0.0029
## 6 1.2694 nan 0.0100 0.0037
## 7 1.2625 nan 0.0100 0.0032
## 8 1.2542 nan 0.0100 0.0039
## 9 1.2470 nan 0.0100 0.0034
## 10 1.2391 nan 0.0100 0.0036
## 20 1.1702 nan 0.0100 0.0027
## 40 1.0574 nan 0.0100 0.0025
## 60 0.9739 nan 0.0100 0.0013
## 80 0.9094 nan 0.0100 0.0012
## 100 0.8563 nan 0.0100 0.0007
## 120 0.8140 nan 0.0100 0.0006
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## 180 0.7233 nan 0.0100 0.0003
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## 220 0.6819 nan 0.0100 0.0002
## 240 0.6654 nan 0.0100 0.0000
## 260 0.6505 nan 0.0100 -0.0000
## 280 0.6355 nan 0.0100 0.0001
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## 360 0.5906 nan 0.0100 0.0000
## 380 0.5809 nan 0.0100 0.0001
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## 460 0.5449 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0044
## 2 1.3027 nan 0.0100 0.0040
## 3 1.2947 nan 0.0100 0.0034
## 4 1.2861 nan 0.0100 0.0039
## 5 1.2781 nan 0.0100 0.0038
## 6 1.2703 nan 0.0100 0.0032
## 7 1.2627 nan 0.0100 0.0035
## 8 1.2543 nan 0.0100 0.0037
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## 10 1.2399 nan 0.0100 0.0035
## 20 1.1713 nan 0.0100 0.0028
## 40 1.0600 nan 0.0100 0.0018
## 60 0.9744 nan 0.0100 0.0016
## 80 0.9093 nan 0.0100 0.0012
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## 200 0.7038 nan 0.0100 0.0003
## 220 0.6857 nan 0.0100 0.0002
## 240 0.6684 nan 0.0100 0.0000
## 260 0.6541 nan 0.0100 0.0001
## 280 0.6412 nan 0.0100 -0.0000
## 300 0.6291 nan 0.0100 0.0000
## 320 0.6175 nan 0.0100 -0.0000
## 340 0.6070 nan 0.0100 0.0000
## 360 0.5960 nan 0.0100 0.0000
## 380 0.5871 nan 0.0100 0.0001
## 400 0.5782 nan 0.0100 0.0000
## 420 0.5692 nan 0.0100 -0.0002
## 440 0.5604 nan 0.0100 -0.0001
## 460 0.5526 nan 0.0100 -0.0001
## 480 0.5446 nan 0.0100 -0.0000
## 500 0.5373 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0043
## 2 1.3015 nan 0.0100 0.0046
## 3 1.2926 nan 0.0100 0.0040
## 4 1.2839 nan 0.0100 0.0040
## 5 1.2755 nan 0.0100 0.0036
## 6 1.2665 nan 0.0100 0.0042
## 7 1.2580 nan 0.0100 0.0038
## 8 1.2493 nan 0.0100 0.0039
## 9 1.2408 nan 0.0100 0.0038
## 10 1.2337 nan 0.0100 0.0032
## 20 1.1601 nan 0.0100 0.0031
## 40 1.0446 nan 0.0100 0.0023
## 60 0.9559 nan 0.0100 0.0017
## 80 0.8868 nan 0.0100 0.0012
## 100 0.8304 nan 0.0100 0.0010
## 120 0.7861 nan 0.0100 0.0009
## 140 0.7481 nan 0.0100 0.0005
## 160 0.7169 nan 0.0100 0.0005
## 180 0.6900 nan 0.0100 0.0003
## 200 0.6667 nan 0.0100 0.0004
## 220 0.6460 nan 0.0100 0.0001
## 240 0.6267 nan 0.0100 0.0001
## 260 0.6095 nan 0.0100 0.0001
## 280 0.5940 nan 0.0100 0.0000
## 300 0.5795 nan 0.0100 0.0001
## 320 0.5667 nan 0.0100 0.0001
## 340 0.5545 nan 0.0100 -0.0002
## 360 0.5424 nan 0.0100 -0.0001
## 380 0.5318 nan 0.0100 -0.0001
## 400 0.5210 nan 0.0100 0.0001
## 420 0.5109 nan 0.0100 -0.0000
## 440 0.5018 nan 0.0100 0.0001
## 460 0.4930 nan 0.0100 0.0001
## 480 0.4835 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3106 nan 0.0100 0.0046
## 2 1.3017 nan 0.0100 0.0039
## 3 1.2933 nan 0.0100 0.0036
## 4 1.2842 nan 0.0100 0.0039
## 5 1.2756 nan 0.0100 0.0042
## 6 1.2680 nan 0.0100 0.0035
## 7 1.2588 nan 0.0100 0.0037
## 8 1.2507 nan 0.0100 0.0033
## 9 1.2419 nan 0.0100 0.0037
## 10 1.2338 nan 0.0100 0.0041
## 20 1.1604 nan 0.0100 0.0029
## 40 1.0453 nan 0.0100 0.0021
## 60 0.9556 nan 0.0100 0.0015
## 80 0.8870 nan 0.0100 0.0011
## 100 0.8312 nan 0.0100 0.0008
## 120 0.7864 nan 0.0100 0.0008
## 140 0.7494 nan 0.0100 0.0004
## 160 0.7178 nan 0.0100 0.0002
## 180 0.6922 nan 0.0100 0.0003
## 200 0.6694 nan 0.0100 0.0002
## 220 0.6498 nan 0.0100 -0.0000
## 240 0.6315 nan 0.0100 0.0002
## 260 0.6152 nan 0.0100 0.0002
## 280 0.6005 nan 0.0100 0.0001
## 300 0.5870 nan 0.0100 0.0000
## 320 0.5732 nan 0.0100 -0.0000
## 340 0.5609 nan 0.0100 -0.0000
## 360 0.5495 nan 0.0100 -0.0001
## 380 0.5387 nan 0.0100 -0.0001
## 400 0.5287 nan 0.0100 -0.0001
## 420 0.5180 nan 0.0100 0.0000
## 440 0.5084 nan 0.0100 0.0001
## 460 0.4989 nan 0.0100 -0.0001
## 480 0.4901 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0047
## 2 1.3012 nan 0.0100 0.0049
## 3 1.2917 nan 0.0100 0.0047
## 4 1.2831 nan 0.0100 0.0039
## 5 1.2736 nan 0.0100 0.0045
## 6 1.2655 nan 0.0100 0.0038
## 7 1.2574 nan 0.0100 0.0040
## 8 1.2493 nan 0.0100 0.0036
## 9 1.2407 nan 0.0100 0.0035
## 10 1.2330 nan 0.0100 0.0032
## 20 1.1589 nan 0.0100 0.0031
## 40 1.0464 nan 0.0100 0.0025
## 60 0.9586 nan 0.0100 0.0016
## 80 0.8905 nan 0.0100 0.0013
## 100 0.8357 nan 0.0100 0.0007
## 120 0.7913 nan 0.0100 0.0006
## 140 0.7533 nan 0.0100 0.0006
## 160 0.7235 nan 0.0100 0.0006
## 180 0.6978 nan 0.0100 0.0001
## 200 0.6742 nan 0.0100 0.0001
## 220 0.6544 nan 0.0100 0.0002
## 240 0.6369 nan 0.0100 0.0002
## 260 0.6202 nan 0.0100 0.0001
## 280 0.6060 nan 0.0100 0.0002
## 300 0.5914 nan 0.0100 0.0000
## 320 0.5781 nan 0.0100 -0.0001
## 340 0.5668 nan 0.0100 -0.0000
## 360 0.5556 nan 0.0100 -0.0001
## 380 0.5443 nan 0.0100 -0.0001
## 400 0.5351 nan 0.0100 -0.0000
## 420 0.5251 nan 0.0100 -0.0001
## 440 0.5153 nan 0.0100 0.0000
## 460 0.5060 nan 0.0100 -0.0002
## 480 0.4974 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.0100 0.0044
## 2 1.3017 nan 0.0100 0.0039
## 3 1.2927 nan 0.0100 0.0037
## 4 1.2836 nan 0.0100 0.0045
## 5 1.2739 nan 0.0100 0.0043
## 6 1.2650 nan 0.0100 0.0041
## 7 1.2566 nan 0.0100 0.0040
## 8 1.2474 nan 0.0100 0.0040
## 9 1.2387 nan 0.0100 0.0042
## 10 1.2297 nan 0.0100 0.0043
## 20 1.1510 nan 0.0100 0.0032
## 40 1.0302 nan 0.0100 0.0019
## 60 0.9386 nan 0.0100 0.0016
## 80 0.8680 nan 0.0100 0.0011
## 100 0.8104 nan 0.0100 0.0011
## 120 0.7640 nan 0.0100 0.0008
## 140 0.7255 nan 0.0100 0.0007
## 160 0.6915 nan 0.0100 0.0003
## 180 0.6626 nan 0.0100 0.0005
## 200 0.6373 nan 0.0100 0.0003
## 220 0.6144 nan 0.0100 0.0000
## 240 0.5954 nan 0.0100 0.0000
## 260 0.5768 nan 0.0100 0.0001
## 280 0.5601 nan 0.0100 0.0000
## 300 0.5455 nan 0.0100 0.0001
## 320 0.5313 nan 0.0100 0.0000
## 340 0.5171 nan 0.0100 0.0001
## 360 0.5049 nan 0.0100 0.0002
## 380 0.4926 nan 0.0100 -0.0001
## 400 0.4809 nan 0.0100 0.0000
## 420 0.4695 nan 0.0100 0.0001
## 440 0.4592 nan 0.0100 -0.0001
## 460 0.4496 nan 0.0100 -0.0002
## 480 0.4402 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3013 nan 0.0100 0.0045
## 3 1.2921 nan 0.0100 0.0037
## 4 1.2831 nan 0.0100 0.0043
## 5 1.2740 nan 0.0100 0.0042
## 6 1.2652 nan 0.0100 0.0042
## 7 1.2565 nan 0.0100 0.0040
## 8 1.2474 nan 0.0100 0.0041
## 9 1.2391 nan 0.0100 0.0039
## 10 1.2311 nan 0.0100 0.0035
## 20 1.1535 nan 0.0100 0.0033
## 40 1.0334 nan 0.0100 0.0024
## 60 0.9422 nan 0.0100 0.0020
## 80 0.8699 nan 0.0100 0.0011
## 100 0.8136 nan 0.0100 0.0007
## 120 0.7653 nan 0.0100 0.0004
## 140 0.7273 nan 0.0100 0.0006
## 160 0.6955 nan 0.0100 0.0004
## 180 0.6672 nan 0.0100 0.0004
## 200 0.6414 nan 0.0100 0.0002
## 220 0.6202 nan 0.0100 0.0002
## 240 0.6009 nan 0.0100 0.0003
## 260 0.5825 nan 0.0100 -0.0000
## 280 0.5664 nan 0.0100 0.0001
## 300 0.5521 nan 0.0100 -0.0000
## 320 0.5383 nan 0.0100 0.0001
## 340 0.5252 nan 0.0100 0.0000
## 360 0.5128 nan 0.0100 0.0001
## 380 0.5014 nan 0.0100 -0.0001
## 400 0.4894 nan 0.0100 -0.0000
## 420 0.4786 nan 0.0100 0.0000
## 440 0.4678 nan 0.0100 0.0001
## 460 0.4574 nan 0.0100 -0.0001
## 480 0.4479 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3015 nan 0.0100 0.0045
## 3 1.2916 nan 0.0100 0.0045
## 4 1.2815 nan 0.0100 0.0044
## 5 1.2724 nan 0.0100 0.0038
## 6 1.2634 nan 0.0100 0.0037
## 7 1.2552 nan 0.0100 0.0039
## 8 1.2466 nan 0.0100 0.0040
## 9 1.2385 nan 0.0100 0.0037
## 10 1.2303 nan 0.0100 0.0039
## 20 1.1551 nan 0.0100 0.0034
## 40 1.0345 nan 0.0100 0.0024
## 60 0.9460 nan 0.0100 0.0018
## 80 0.8750 nan 0.0100 0.0013
## 100 0.8180 nan 0.0100 0.0012
## 120 0.7737 nan 0.0100 0.0007
## 140 0.7361 nan 0.0100 0.0003
## 160 0.7041 nan 0.0100 0.0004
## 180 0.6763 nan 0.0100 0.0003
## 200 0.6520 nan 0.0100 0.0001
## 220 0.6301 nan 0.0100 -0.0000
## 240 0.6115 nan 0.0100 0.0002
## 260 0.5947 nan 0.0100 0.0001
## 280 0.5786 nan 0.0100 0.0002
## 300 0.5634 nan 0.0100 0.0000
## 320 0.5501 nan 0.0100 -0.0001
## 340 0.5362 nan 0.0100 -0.0002
## 360 0.5239 nan 0.0100 0.0000
## 380 0.5113 nan 0.0100 0.0001
## 400 0.5002 nan 0.0100 -0.0001
## 420 0.4894 nan 0.0100 -0.0000
## 440 0.4796 nan 0.0100 -0.0002
## 460 0.4697 nan 0.0100 0.0002
## 480 0.4598 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0405
## 2 1.1692 nan 0.1000 0.0304
## 3 1.1095 nan 0.1000 0.0311
## 4 1.0607 nan 0.1000 0.0175
## 5 1.0082 nan 0.1000 0.0223
## 6 0.9687 nan 0.1000 0.0167
## 7 0.9350 nan 0.1000 0.0138
## 8 0.9013 nan 0.1000 0.0127
## 9 0.8744 nan 0.1000 0.0105
## 10 0.8507 nan 0.1000 0.0086
## 20 0.7061 nan 0.1000 0.0030
## 40 0.5755 nan 0.1000 -0.0006
## 60 0.4942 nan 0.1000 -0.0003
## 80 0.4354 nan 0.1000 0.0004
## 100 0.3833 nan 0.1000 -0.0005
## 120 0.3425 nan 0.1000 -0.0017
## 140 0.3034 nan 0.1000 -0.0007
## 160 0.2735 nan 0.1000 -0.0001
## 180 0.2482 nan 0.1000 -0.0006
## 200 0.2198 nan 0.1000 -0.0010
## 220 0.2005 nan 0.1000 -0.0002
## 240 0.1834 nan 0.1000 -0.0003
## 260 0.1671 nan 0.1000 -0.0004
## 280 0.1527 nan 0.1000 -0.0004
## 300 0.1407 nan 0.1000 -0.0004
## 320 0.1284 nan 0.1000 -0.0003
## 340 0.1187 nan 0.1000 -0.0003
## 360 0.1092 nan 0.1000 -0.0001
## 380 0.1000 nan 0.1000 -0.0001
## 400 0.0927 nan 0.1000 -0.0002
## 420 0.0849 nan 0.1000 -0.0002
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## 460 0.0735 nan 0.1000 -0.0001
## 480 0.0682 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0389
## 2 1.1719 nan 0.1000 0.0227
## 3 1.1175 nan 0.1000 0.0235
## 4 1.0626 nan 0.1000 0.0244
## 5 1.0158 nan 0.1000 0.0202
## 6 0.9778 nan 0.1000 0.0158
## 7 0.9431 nan 0.1000 0.0131
## 8 0.9037 nan 0.1000 0.0158
## 9 0.8779 nan 0.1000 0.0104
## 10 0.8516 nan 0.1000 0.0097
## 20 0.7113 nan 0.1000 -0.0004
## 40 0.5795 nan 0.1000 0.0010
## 60 0.5005 nan 0.1000 -0.0006
## 80 0.4415 nan 0.1000 -0.0021
## 100 0.3931 nan 0.1000 -0.0020
## 120 0.3505 nan 0.1000 -0.0005
## 140 0.3131 nan 0.1000 0.0003
## 160 0.2790 nan 0.1000 -0.0004
## 180 0.2525 nan 0.1000 -0.0005
## 200 0.2289 nan 0.1000 -0.0007
## 220 0.2082 nan 0.1000 -0.0007
## 240 0.1897 nan 0.1000 -0.0005
## 260 0.1733 nan 0.1000 -0.0002
## 280 0.1582 nan 0.1000 -0.0006
## 300 0.1436 nan 0.1000 -0.0007
## 320 0.1326 nan 0.1000 -0.0004
## 340 0.1219 nan 0.1000 -0.0003
## 360 0.1116 nan 0.1000 -0.0002
## 380 0.1028 nan 0.1000 -0.0000
## 400 0.0958 nan 0.1000 -0.0003
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## 440 0.0811 nan 0.1000 -0.0004
## 460 0.0753 nan 0.1000 -0.0001
## 480 0.0697 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2321 nan 0.1000 0.0392
## 2 1.1564 nan 0.1000 0.0367
## 3 1.0986 nan 0.1000 0.0277
## 4 1.0515 nan 0.1000 0.0206
## 5 1.0080 nan 0.1000 0.0194
## 6 0.9677 nan 0.1000 0.0188
## 7 0.9358 nan 0.1000 0.0124
## 8 0.9005 nan 0.1000 0.0142
## 9 0.8730 nan 0.1000 0.0105
## 10 0.8466 nan 0.1000 0.0114
## 20 0.6934 nan 0.1000 0.0023
## 40 0.5712 nan 0.1000 -0.0003
## 60 0.5006 nan 0.1000 -0.0016
## 80 0.4457 nan 0.1000 -0.0019
## 100 0.3930 nan 0.1000 -0.0009
## 120 0.3486 nan 0.1000 -0.0014
## 140 0.3184 nan 0.1000 -0.0011
## 160 0.2861 nan 0.1000 -0.0008
## 180 0.2644 nan 0.1000 -0.0009
## 200 0.2395 nan 0.1000 -0.0009
## 220 0.2199 nan 0.1000 -0.0018
## 240 0.2010 nan 0.1000 -0.0002
## 260 0.1854 nan 0.1000 -0.0009
## 280 0.1703 nan 0.1000 -0.0010
## 300 0.1575 nan 0.1000 -0.0006
## 320 0.1449 nan 0.1000 -0.0009
## 340 0.1314 nan 0.1000 -0.0002
## 360 0.1212 nan 0.1000 -0.0003
## 380 0.1121 nan 0.1000 -0.0007
## 400 0.1043 nan 0.1000 -0.0006
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## 460 0.0830 nan 0.1000 -0.0004
## 480 0.0774 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.1000 0.0393
## 2 1.1512 nan 0.1000 0.0391
## 3 1.0878 nan 0.1000 0.0272
## 4 1.0339 nan 0.1000 0.0247
## 5 0.9880 nan 0.1000 0.0185
## 6 0.9479 nan 0.1000 0.0148
## 7 0.9043 nan 0.1000 0.0184
## 8 0.8661 nan 0.1000 0.0149
## 9 0.8369 nan 0.1000 0.0110
## 10 0.8136 nan 0.1000 0.0088
## 20 0.6606 nan 0.1000 0.0040
## 40 0.5210 nan 0.1000 -0.0003
## 60 0.4408 nan 0.1000 -0.0002
## 80 0.3806 nan 0.1000 -0.0004
## 100 0.3337 nan 0.1000 -0.0017
## 120 0.2903 nan 0.1000 -0.0002
## 140 0.2539 nan 0.1000 -0.0007
## 160 0.2208 nan 0.1000 -0.0008
## 180 0.1963 nan 0.1000 -0.0004
## 200 0.1752 nan 0.1000 -0.0010
## 220 0.1555 nan 0.1000 -0.0006
## 240 0.1380 nan 0.1000 -0.0002
## 260 0.1238 nan 0.1000 -0.0001
## 280 0.1087 nan 0.1000 -0.0003
## 300 0.0979 nan 0.1000 -0.0005
## 320 0.0887 nan 0.1000 -0.0003
## 340 0.0800 nan 0.1000 -0.0002
## 360 0.0717 nan 0.1000 -0.0000
## 380 0.0642 nan 0.1000 -0.0001
## 400 0.0577 nan 0.1000 0.0000
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## 460 0.0432 nan 0.1000 -0.0003
## 480 0.0392 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2282 nan 0.1000 0.0431
## 2 1.1537 nan 0.1000 0.0291
## 3 1.0933 nan 0.1000 0.0303
## 4 1.0353 nan 0.1000 0.0240
## 5 0.9882 nan 0.1000 0.0202
## 6 0.9473 nan 0.1000 0.0159
## 7 0.9139 nan 0.1000 0.0142
## 8 0.8803 nan 0.1000 0.0154
## 9 0.8508 nan 0.1000 0.0108
## 10 0.8249 nan 0.1000 0.0092
## 20 0.6708 nan 0.1000 0.0035
## 40 0.5247 nan 0.1000 -0.0006
## 60 0.4458 nan 0.1000 -0.0003
## 80 0.3874 nan 0.1000 -0.0009
## 100 0.3330 nan 0.1000 -0.0010
## 120 0.2922 nan 0.1000 -0.0009
## 140 0.2581 nan 0.1000 -0.0005
## 160 0.2277 nan 0.1000 -0.0010
## 180 0.2007 nan 0.1000 -0.0007
## 200 0.1746 nan 0.1000 -0.0002
## 220 0.1557 nan 0.1000 -0.0007
## 240 0.1387 nan 0.1000 -0.0005
## 260 0.1238 nan 0.1000 -0.0007
## 280 0.1095 nan 0.1000 -0.0003
## 300 0.0991 nan 0.1000 -0.0006
## 320 0.0884 nan 0.1000 -0.0002
## 340 0.0800 nan 0.1000 -0.0003
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## 380 0.0654 nan 0.1000 -0.0002
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## 480 0.0405 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2327 nan 0.1000 0.0386
## 2 1.1570 nan 0.1000 0.0321
## 3 1.0957 nan 0.1000 0.0276
## 4 1.0404 nan 0.1000 0.0227
## 5 0.9950 nan 0.1000 0.0198
## 6 0.9520 nan 0.1000 0.0199
## 7 0.9171 nan 0.1000 0.0135
## 8 0.8858 nan 0.1000 0.0131
## 9 0.8586 nan 0.1000 0.0106
## 10 0.8344 nan 0.1000 0.0094
## 20 0.6821 nan 0.1000 0.0020
## 40 0.5429 nan 0.1000 -0.0007
## 60 0.4664 nan 0.1000 0.0003
## 80 0.3961 nan 0.1000 -0.0016
## 100 0.3438 nan 0.1000 -0.0008
## 120 0.3024 nan 0.1000 -0.0005
## 140 0.2675 nan 0.1000 -0.0007
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## 180 0.2135 nan 0.1000 -0.0006
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## 280 0.1231 nan 0.1000 -0.0004
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## 320 0.0993 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0346
## 2 1.1571 nan 0.1000 0.0344
## 3 1.0977 nan 0.1000 0.0257
## 4 1.0409 nan 0.1000 0.0270
## 5 0.9903 nan 0.1000 0.0203
## 6 0.9452 nan 0.1000 0.0175
## 7 0.9018 nan 0.1000 0.0158
## 8 0.8686 nan 0.1000 0.0123
## 9 0.8355 nan 0.1000 0.0128
## 10 0.8071 nan 0.1000 0.0108
## 20 0.6300 nan 0.1000 0.0037
## 40 0.4813 nan 0.1000 -0.0010
## 60 0.3973 nan 0.1000 -0.0007
## 80 0.3374 nan 0.1000 -0.0013
## 100 0.2818 nan 0.1000 -0.0005
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## 180 0.1428 nan 0.1000 -0.0006
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## 280 0.0731 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2295 nan 0.1000 0.0449
## 2 1.1563 nan 0.1000 0.0319
## 3 1.0953 nan 0.1000 0.0245
## 4 1.0412 nan 0.1000 0.0236
## 5 0.9897 nan 0.1000 0.0210
## 6 0.9450 nan 0.1000 0.0196
## 7 0.9100 nan 0.1000 0.0147
## 8 0.8732 nan 0.1000 0.0138
## 9 0.8414 nan 0.1000 0.0129
## 10 0.8153 nan 0.1000 0.0096
## 20 0.6412 nan 0.1000 0.0020
## 40 0.4965 nan 0.1000 -0.0014
## 60 0.4056 nan 0.1000 -0.0001
## 80 0.3344 nan 0.1000 -0.0013
## 100 0.2833 nan 0.1000 -0.0001
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## 140 0.2043 nan 0.1000 -0.0011
## 160 0.1778 nan 0.1000 -0.0007
## 180 0.1549 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0380
## 2 1.1560 nan 0.1000 0.0391
## 3 1.0902 nan 0.1000 0.0264
## 4 1.0351 nan 0.1000 0.0220
## 5 0.9853 nan 0.1000 0.0200
## 6 0.9378 nan 0.1000 0.0182
## 7 0.9012 nan 0.1000 0.0139
## 8 0.8689 nan 0.1000 0.0116
## 9 0.8385 nan 0.1000 0.0118
## 10 0.8131 nan 0.1000 0.0103
## 20 0.6441 nan 0.1000 0.0029
## 40 0.5068 nan 0.1000 -0.0000
## 60 0.4147 nan 0.1000 -0.0011
## 80 0.3472 nan 0.1000 -0.0018
## 100 0.2881 nan 0.1000 -0.0006
## 120 0.2494 nan 0.1000 -0.0002
## 140 0.2124 nan 0.1000 -0.0002
## 160 0.1819 nan 0.1000 -0.0002
## 180 0.1594 nan 0.1000 -0.0003
## 200 0.1390 nan 0.1000 -0.0006
## 220 0.1212 nan 0.1000 -0.0008
## 240 0.1074 nan 0.1000 -0.0006
## 260 0.0946 nan 0.1000 -0.0003
## 280 0.0838 nan 0.1000 -0.0002
## 300 0.0740 nan 0.1000 0.0001
## 320 0.0650 nan 0.1000 -0.0002
## 340 0.0575 nan 0.1000 -0.0002
## 360 0.0512 nan 0.1000 -0.0002
## 380 0.0458 nan 0.1000 -0.0002
## 400 0.0405 nan 0.1000 -0.0001
## 420 0.0357 nan 0.1000 -0.0001
## 440 0.0322 nan 0.1000 -0.0002
## 460 0.0290 nan 0.1000 -0.0002
## 480 0.0259 nan 0.1000 -0.0002
## 500 0.0233 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3205 nan 0.0010 0.0003
## 2 1.3196 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3179 nan 0.0010 0.0004
## 5 1.3172 nan 0.0010 0.0003
## 6 1.3164 nan 0.0010 0.0004
## 7 1.3156 nan 0.0010 0.0004
## 8 1.3148 nan 0.0010 0.0004
## 9 1.3140 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2892 nan 0.0010 0.0003
## 60 1.2738 nan 0.0010 0.0003
## 80 1.2591 nan 0.0010 0.0003
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2309 nan 0.0010 0.0003
## 140 1.2174 nan 0.0010 0.0003
## 160 1.2041 nan 0.0010 0.0003
## 180 1.1916 nan 0.0010 0.0002
## 200 1.1797 nan 0.0010 0.0002
## 220 1.1681 nan 0.0010 0.0003
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## 280 1.1349 nan 0.0010 0.0002
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## 320 1.1141 nan 0.0010 0.0002
## 340 1.1044 nan 0.0010 0.0002
## 360 1.0946 nan 0.0010 0.0002
## 380 1.0852 nan 0.0010 0.0002
## 400 1.0759 nan 0.0010 0.0002
## 420 1.0671 nan 0.0010 0.0002
## 440 1.0588 nan 0.0010 0.0001
## 460 1.0504 nan 0.0010 0.0002
## 480 1.0421 nan 0.0010 0.0002
## 500 1.0340 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0003
## 4 1.3179 nan 0.0010 0.0003
## 5 1.3170 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3145 nan 0.0010 0.0003
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0004
## 40 1.2890 nan 0.0010 0.0004
## 60 1.2736 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0004
## 100 1.2446 nan 0.0010 0.0003
## 120 1.2310 nan 0.0010 0.0003
## 140 1.2176 nan 0.0010 0.0003
## 160 1.2046 nan 0.0010 0.0003
## 180 1.1920 nan 0.0010 0.0003
## 200 1.1797 nan 0.0010 0.0003
## 220 1.1680 nan 0.0010 0.0003
## 240 1.1568 nan 0.0010 0.0002
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## 280 1.1348 nan 0.0010 0.0002
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## 320 1.1142 nan 0.0010 0.0002
## 340 1.1042 nan 0.0010 0.0002
## 360 1.0947 nan 0.0010 0.0002
## 380 1.0852 nan 0.0010 0.0002
## 400 1.0760 nan 0.0010 0.0002
## 420 1.0673 nan 0.0010 0.0002
## 440 1.0586 nan 0.0010 0.0002
## 460 1.0505 nan 0.0010 0.0002
## 480 1.0425 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3180 nan 0.0010 0.0004
## 5 1.3171 nan 0.0010 0.0004
## 6 1.3164 nan 0.0010 0.0004
## 7 1.3155 nan 0.0010 0.0004
## 8 1.3147 nan 0.0010 0.0004
## 9 1.3139 nan 0.0010 0.0004
## 10 1.3131 nan 0.0010 0.0004
## 20 1.3053 nan 0.0010 0.0004
## 40 1.2897 nan 0.0010 0.0003
## 60 1.2747 nan 0.0010 0.0003
## 80 1.2601 nan 0.0010 0.0003
## 100 1.2457 nan 0.0010 0.0003
## 120 1.2319 nan 0.0010 0.0003
## 140 1.2189 nan 0.0010 0.0003
## 160 1.2060 nan 0.0010 0.0003
## 180 1.1935 nan 0.0010 0.0003
## 200 1.1814 nan 0.0010 0.0002
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1582 nan 0.0010 0.0003
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## 280 1.1364 nan 0.0010 0.0002
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## 320 1.1157 nan 0.0010 0.0002
## 340 1.1058 nan 0.0010 0.0002
## 360 1.0961 nan 0.0010 0.0002
## 380 1.0866 nan 0.0010 0.0002
## 400 1.0777 nan 0.0010 0.0002
## 420 1.0687 nan 0.0010 0.0002
## 440 1.0599 nan 0.0010 0.0001
## 460 1.0514 nan 0.0010 0.0002
## 480 1.0434 nan 0.0010 0.0002
## 500 1.0353 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3177 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0003
## 9 1.3135 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2706 nan 0.0010 0.0003
## 80 1.2550 nan 0.0010 0.0003
## 100 1.2395 nan 0.0010 0.0003
## 120 1.2247 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1967 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1705 nan 0.0010 0.0003
## 220 1.1581 nan 0.0010 0.0003
## 240 1.1458 nan 0.0010 0.0003
## 260 1.1340 nan 0.0010 0.0002
## 280 1.1224 nan 0.0010 0.0002
## 300 1.1112 nan 0.0010 0.0002
## 320 1.1002 nan 0.0010 0.0002
## 340 1.0895 nan 0.0010 0.0002
## 360 1.0796 nan 0.0010 0.0002
## 380 1.0695 nan 0.0010 0.0002
## 400 1.0596 nan 0.0010 0.0002
## 420 1.0501 nan 0.0010 0.0002
## 440 1.0411 nan 0.0010 0.0002
## 460 1.0319 nan 0.0010 0.0002
## 480 1.0233 nan 0.0010 0.0002
## 500 1.0149 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3187 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3169 nan 0.0010 0.0004
## 6 1.3161 nan 0.0010 0.0004
## 7 1.3152 nan 0.0010 0.0004
## 8 1.3144 nan 0.0010 0.0004
## 9 1.3136 nan 0.0010 0.0004
## 10 1.3128 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0003
## 60 1.2711 nan 0.0010 0.0003
## 80 1.2557 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0004
## 120 1.2258 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1983 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1594 nan 0.0010 0.0003
## 240 1.1473 nan 0.0010 0.0002
## 260 1.1356 nan 0.0010 0.0002
## 280 1.1241 nan 0.0010 0.0003
## 300 1.1130 nan 0.0010 0.0002
## 320 1.1021 nan 0.0010 0.0002
## 340 1.0914 nan 0.0010 0.0002
## 360 1.0810 nan 0.0010 0.0002
## 380 1.0707 nan 0.0010 0.0002
## 400 1.0611 nan 0.0010 0.0002
## 420 1.0517 nan 0.0010 0.0002
## 440 1.0425 nan 0.0010 0.0002
## 460 1.0340 nan 0.0010 0.0002
## 480 1.0252 nan 0.0010 0.0002
## 500 1.0168 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3160 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0003
## 60 1.2709 nan 0.0010 0.0004
## 80 1.2553 nan 0.0010 0.0003
## 100 1.2400 nan 0.0010 0.0004
## 120 1.2256 nan 0.0010 0.0003
## 140 1.2115 nan 0.0010 0.0003
## 160 1.1978 nan 0.0010 0.0003
## 180 1.1847 nan 0.0010 0.0003
## 200 1.1720 nan 0.0010 0.0003
## 220 1.1596 nan 0.0010 0.0003
## 240 1.1477 nan 0.0010 0.0003
## 260 1.1357 nan 0.0010 0.0003
## 280 1.1241 nan 0.0010 0.0002
## 300 1.1132 nan 0.0010 0.0002
## 320 1.1023 nan 0.0010 0.0002
## 340 1.0920 nan 0.0010 0.0002
## 360 1.0821 nan 0.0010 0.0002
## 380 1.0721 nan 0.0010 0.0002
## 400 1.0624 nan 0.0010 0.0002
## 420 1.0530 nan 0.0010 0.0002
## 440 1.0437 nan 0.0010 0.0002
## 460 1.0344 nan 0.0010 0.0002
## 480 1.0259 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0005
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2678 nan 0.0010 0.0003
## 80 1.2513 nan 0.0010 0.0004
## 100 1.2353 nan 0.0010 0.0004
## 120 1.2196 nan 0.0010 0.0003
## 140 1.2046 nan 0.0010 0.0004
## 160 1.1900 nan 0.0010 0.0003
## 180 1.1758 nan 0.0010 0.0004
## 200 1.1623 nan 0.0010 0.0003
## 220 1.1496 nan 0.0010 0.0003
## 240 1.1370 nan 0.0010 0.0003
## 260 1.1249 nan 0.0010 0.0003
## 280 1.1128 nan 0.0010 0.0003
## 300 1.1012 nan 0.0010 0.0003
## 320 1.0899 nan 0.0010 0.0002
## 340 1.0789 nan 0.0010 0.0003
## 360 1.0685 nan 0.0010 0.0002
## 380 1.0581 nan 0.0010 0.0002
## 400 1.0480 nan 0.0010 0.0002
## 420 1.0383 nan 0.0010 0.0002
## 440 1.0291 nan 0.0010 0.0002
## 460 1.0198 nan 0.0010 0.0002
## 480 1.0109 nan 0.0010 0.0002
## 500 1.0019 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0003
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0005
## 20 1.3031 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0003
## 80 1.2520 nan 0.0010 0.0003
## 100 1.2358 nan 0.0010 0.0003
## 120 1.2206 nan 0.0010 0.0004
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1918 nan 0.0010 0.0003
## 180 1.1778 nan 0.0010 0.0003
## 200 1.1643 nan 0.0010 0.0003
## 220 1.1512 nan 0.0010 0.0003
## 240 1.1388 nan 0.0010 0.0003
## 260 1.1266 nan 0.0010 0.0002
## 280 1.1146 nan 0.0010 0.0003
## 300 1.1029 nan 0.0010 0.0002
## 320 1.0919 nan 0.0010 0.0002
## 340 1.0810 nan 0.0010 0.0002
## 360 1.0707 nan 0.0010 0.0002
## 380 1.0605 nan 0.0010 0.0002
## 400 1.0508 nan 0.0010 0.0002
## 420 1.0412 nan 0.0010 0.0002
## 440 1.0318 nan 0.0010 0.0002
## 460 1.0223 nan 0.0010 0.0002
## 480 1.0132 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0003
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0004
## 100 1.2369 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2072 nan 0.0010 0.0003
## 160 1.1935 nan 0.0010 0.0002
## 180 1.1798 nan 0.0010 0.0003
## 200 1.1665 nan 0.0010 0.0003
## 220 1.1538 nan 0.0010 0.0002
## 240 1.1413 nan 0.0010 0.0003
## 260 1.1292 nan 0.0010 0.0002
## 280 1.1177 nan 0.0010 0.0002
## 300 1.1059 nan 0.0010 0.0003
## 320 1.0947 nan 0.0010 0.0002
## 340 1.0840 nan 0.0010 0.0002
## 360 1.0736 nan 0.0010 0.0003
## 380 1.0633 nan 0.0010 0.0002
## 400 1.0532 nan 0.0010 0.0002
## 420 1.0434 nan 0.0010 0.0002
## 440 1.0337 nan 0.0010 0.0002
## 460 1.0245 nan 0.0010 0.0002
## 480 1.0157 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0039
## 3 1.2959 nan 0.0100 0.0034
## 4 1.2880 nan 0.0100 0.0038
## 5 1.2807 nan 0.0100 0.0031
## 6 1.2726 nan 0.0100 0.0038
## 7 1.2652 nan 0.0100 0.0033
## 8 1.2574 nan 0.0100 0.0035
## 9 1.2504 nan 0.0100 0.0031
## 10 1.2435 nan 0.0100 0.0030
## 20 1.1791 nan 0.0100 0.0029
## 40 1.0735 nan 0.0100 0.0020
## 60 0.9945 nan 0.0100 0.0016
## 80 0.9333 nan 0.0100 0.0012
## 100 0.8842 nan 0.0100 0.0008
## 120 0.8432 nan 0.0100 0.0006
## 140 0.8089 nan 0.0100 0.0005
## 160 0.7786 nan 0.0100 0.0003
## 180 0.7524 nan 0.0100 0.0003
## 200 0.7298 nan 0.0100 0.0003
## 220 0.7097 nan 0.0100 0.0003
## 240 0.6914 nan 0.0100 0.0003
## 260 0.6757 nan 0.0100 -0.0001
## 280 0.6616 nan 0.0100 0.0000
## 300 0.6490 nan 0.0100 0.0000
## 320 0.6367 nan 0.0100 0.0001
## 340 0.6247 nan 0.0100 0.0000
## 360 0.6136 nan 0.0100 -0.0001
## 380 0.6030 nan 0.0100 -0.0000
## 400 0.5928 nan 0.0100 -0.0002
## 420 0.5825 nan 0.0100 -0.0002
## 440 0.5737 nan 0.0100 0.0001
## 460 0.5642 nan 0.0100 0.0000
## 480 0.5551 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0036
## 2 1.3057 nan 0.0100 0.0033
## 3 1.2979 nan 0.0100 0.0035
## 4 1.2906 nan 0.0100 0.0030
## 5 1.2839 nan 0.0100 0.0033
## 6 1.2764 nan 0.0100 0.0035
## 7 1.2697 nan 0.0100 0.0028
## 8 1.2619 nan 0.0100 0.0038
## 9 1.2545 nan 0.0100 0.0035
## 10 1.2471 nan 0.0100 0.0035
## 20 1.1810 nan 0.0100 0.0028
## 40 1.0764 nan 0.0100 0.0020
## 60 0.9969 nan 0.0100 0.0015
## 80 0.9351 nan 0.0100 0.0012
## 100 0.8833 nan 0.0100 0.0008
## 120 0.8428 nan 0.0100 0.0005
## 140 0.8092 nan 0.0100 0.0005
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## 180 0.7541 nan 0.0100 0.0002
## 200 0.7322 nan 0.0100 0.0003
## 220 0.7126 nan 0.0100 0.0004
## 240 0.6954 nan 0.0100 0.0000
## 260 0.6814 nan 0.0100 -0.0001
## 280 0.6658 nan 0.0100 0.0002
## 300 0.6535 nan 0.0100 -0.0000
## 320 0.6410 nan 0.0100 0.0003
## 340 0.6295 nan 0.0100 0.0000
## 360 0.6187 nan 0.0100 0.0001
## 380 0.6074 nan 0.0100 0.0000
## 400 0.5972 nan 0.0100 0.0001
## 420 0.5867 nan 0.0100 0.0002
## 440 0.5780 nan 0.0100 -0.0001
## 460 0.5686 nan 0.0100 -0.0002
## 480 0.5602 nan 0.0100 -0.0001
## 500 0.5519 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3130 nan 0.0100 0.0038
## 2 1.3049 nan 0.0100 0.0033
## 3 1.2971 nan 0.0100 0.0032
## 4 1.2893 nan 0.0100 0.0031
## 5 1.2823 nan 0.0100 0.0033
## 6 1.2747 nan 0.0100 0.0034
## 7 1.2680 nan 0.0100 0.0032
## 8 1.2608 nan 0.0100 0.0035
## 9 1.2541 nan 0.0100 0.0028
## 10 1.2471 nan 0.0100 0.0028
## 20 1.1814 nan 0.0100 0.0025
## 40 1.0768 nan 0.0100 0.0019
## 60 0.9979 nan 0.0100 0.0015
## 80 0.9341 nan 0.0100 0.0012
## 100 0.8837 nan 0.0100 0.0008
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## 240 0.7010 nan 0.0100 0.0003
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## 300 0.6595 nan 0.0100 -0.0000
## 320 0.6475 nan 0.0100 0.0001
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## 360 0.6250 nan 0.0100 -0.0000
## 380 0.6136 nan 0.0100 0.0000
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## 460 0.5781 nan 0.0100 -0.0000
## 480 0.5701 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0044
## 2 1.3038 nan 0.0100 0.0037
## 3 1.2946 nan 0.0100 0.0042
## 4 1.2865 nan 0.0100 0.0037
## 5 1.2781 nan 0.0100 0.0036
## 6 1.2700 nan 0.0100 0.0034
## 7 1.2623 nan 0.0100 0.0030
## 8 1.2534 nan 0.0100 0.0037
## 9 1.2463 nan 0.0100 0.0032
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## 20 1.1702 nan 0.0100 0.0026
## 40 1.0608 nan 0.0100 0.0022
## 60 0.9742 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0038
## 2 1.3040 nan 0.0100 0.0034
## 3 1.2956 nan 0.0100 0.0038
## 4 1.2864 nan 0.0100 0.0044
## 5 1.2790 nan 0.0100 0.0034
## 6 1.2714 nan 0.0100 0.0035
## 7 1.2627 nan 0.0100 0.0040
## 8 1.2554 nan 0.0100 0.0031
## 9 1.2482 nan 0.0100 0.0029
## 10 1.2405 nan 0.0100 0.0035
## 20 1.1708 nan 0.0100 0.0032
## 40 1.0604 nan 0.0100 0.0023
## 60 0.9772 nan 0.0100 0.0018
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## 100 0.8581 nan 0.0100 0.0009
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## 260 0.6402 nan 0.0100 -0.0000
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## 400 0.5491 nan 0.0100 0.0001
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## 460 0.5192 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0038
## 2 1.3038 nan 0.0100 0.0035
## 3 1.2954 nan 0.0100 0.0033
## 4 1.2876 nan 0.0100 0.0033
## 5 1.2793 nan 0.0100 0.0035
## 6 1.2722 nan 0.0100 0.0033
## 7 1.2645 nan 0.0100 0.0035
## 8 1.2557 nan 0.0100 0.0041
## 9 1.2471 nan 0.0100 0.0039
## 10 1.2390 nan 0.0100 0.0033
## 20 1.1718 nan 0.0100 0.0029
## 40 1.0603 nan 0.0100 0.0021
## 60 0.9801 nan 0.0100 0.0016
## 80 0.9140 nan 0.0100 0.0010
## 100 0.8608 nan 0.0100 0.0008
## 120 0.8182 nan 0.0100 0.0007
## 140 0.7837 nan 0.0100 0.0005
## 160 0.7539 nan 0.0100 0.0002
## 180 0.7276 nan 0.0100 0.0003
## 200 0.7034 nan 0.0100 0.0003
## 220 0.6825 nan 0.0100 0.0002
## 240 0.6633 nan 0.0100 0.0001
## 260 0.6476 nan 0.0100 0.0001
## 280 0.6318 nan 0.0100 0.0001
## 300 0.6173 nan 0.0100 -0.0002
## 320 0.6036 nan 0.0100 0.0001
## 340 0.5909 nan 0.0100 -0.0001
## 360 0.5791 nan 0.0100 -0.0000
## 380 0.5680 nan 0.0100 0.0001
## 400 0.5577 nan 0.0100 -0.0000
## 420 0.5475 nan 0.0100 -0.0001
## 440 0.5371 nan 0.0100 0.0000
## 460 0.5272 nan 0.0100 -0.0001
## 480 0.5173 nan 0.0100 -0.0001
## 500 0.5072 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0039
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2866 nan 0.0100 0.0033
## 5 1.2771 nan 0.0100 0.0038
## 6 1.2688 nan 0.0100 0.0037
## 7 1.2607 nan 0.0100 0.0036
## 8 1.2528 nan 0.0100 0.0034
## 9 1.2443 nan 0.0100 0.0042
## 10 1.2359 nan 0.0100 0.0036
## 20 1.1631 nan 0.0100 0.0033
## 40 1.0477 nan 0.0100 0.0019
## 60 0.9605 nan 0.0100 0.0016
## 80 0.8927 nan 0.0100 0.0009
## 100 0.8355 nan 0.0100 0.0011
## 120 0.7902 nan 0.0100 0.0005
## 140 0.7516 nan 0.0100 0.0006
## 160 0.7171 nan 0.0100 0.0005
## 180 0.6882 nan 0.0100 0.0003
## 200 0.6625 nan 0.0100 0.0003
## 220 0.6398 nan 0.0100 0.0003
## 240 0.6190 nan 0.0100 0.0000
## 260 0.5993 nan 0.0100 0.0001
## 280 0.5817 nan 0.0100 -0.0001
## 300 0.5650 nan 0.0100 0.0001
## 320 0.5502 nan 0.0100 0.0001
## 340 0.5363 nan 0.0100 -0.0000
## 360 0.5233 nan 0.0100 0.0000
## 380 0.5096 nan 0.0100 -0.0001
## 400 0.4965 nan 0.0100 0.0000
## 420 0.4852 nan 0.0100 -0.0001
## 440 0.4741 nan 0.0100 0.0000
## 460 0.4627 nan 0.0100 0.0000
## 480 0.4523 nan 0.0100 -0.0000
## 500 0.4419 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0048
## 2 1.3029 nan 0.0100 0.0037
## 3 1.2936 nan 0.0100 0.0040
## 4 1.2846 nan 0.0100 0.0038
## 5 1.2754 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0043
## 7 1.2584 nan 0.0100 0.0036
## 8 1.2508 nan 0.0100 0.0034
## 9 1.2418 nan 0.0100 0.0040
## 10 1.2339 nan 0.0100 0.0032
## 20 1.1629 nan 0.0100 0.0030
## 40 1.0495 nan 0.0100 0.0022
## 60 0.9608 nan 0.0100 0.0018
## 80 0.8916 nan 0.0100 0.0012
## 100 0.8383 nan 0.0100 0.0009
## 120 0.7937 nan 0.0100 0.0007
## 140 0.7566 nan 0.0100 0.0006
## 160 0.7232 nan 0.0100 0.0004
## 180 0.6944 nan 0.0100 0.0004
## 200 0.6705 nan 0.0100 0.0001
## 220 0.6494 nan 0.0100 0.0000
## 240 0.6283 nan 0.0100 0.0003
## 260 0.6087 nan 0.0100 -0.0000
## 280 0.5914 nan 0.0100 -0.0000
## 300 0.5756 nan 0.0100 0.0000
## 320 0.5611 nan 0.0100 -0.0001
## 340 0.5464 nan 0.0100 -0.0000
## 360 0.5338 nan 0.0100 -0.0000
## 380 0.5207 nan 0.0100 -0.0000
## 400 0.5079 nan 0.0100 -0.0002
## 420 0.4964 nan 0.0100 0.0000
## 440 0.4847 nan 0.0100 0.0001
## 460 0.4738 nan 0.0100 -0.0001
## 480 0.4637 nan 0.0100 0.0001
## 500 0.4532 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3128 nan 0.0100 0.0037
## 2 1.3045 nan 0.0100 0.0036
## 3 1.2961 nan 0.0100 0.0034
## 4 1.2880 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0037
## 6 1.2710 nan 0.0100 0.0039
## 7 1.2627 nan 0.0100 0.0034
## 8 1.2544 nan 0.0100 0.0039
## 9 1.2466 nan 0.0100 0.0034
## 10 1.2388 nan 0.0100 0.0038
## 20 1.1685 nan 0.0100 0.0026
## 40 1.0516 nan 0.0100 0.0024
## 60 0.9663 nan 0.0100 0.0017
## 80 0.8980 nan 0.0100 0.0010
## 100 0.8418 nan 0.0100 0.0008
## 120 0.7976 nan 0.0100 0.0008
## 140 0.7607 nan 0.0100 0.0006
## 160 0.7300 nan 0.0100 0.0003
## 180 0.7017 nan 0.0100 0.0005
## 200 0.6774 nan 0.0100 0.0001
## 220 0.6575 nan 0.0100 0.0001
## 240 0.6381 nan 0.0100 0.0002
## 260 0.6189 nan 0.0100 0.0003
## 280 0.6012 nan 0.0100 0.0000
## 300 0.5855 nan 0.0100 0.0005
## 320 0.5707 nan 0.0100 0.0002
## 340 0.5562 nan 0.0100 0.0001
## 360 0.5427 nan 0.0100 -0.0000
## 380 0.5296 nan 0.0100 0.0001
## 400 0.5170 nan 0.0100 0.0001
## 420 0.5059 nan 0.0100 0.0000
## 440 0.4948 nan 0.0100 -0.0002
## 460 0.4836 nan 0.0100 0.0001
## 480 0.4722 nan 0.0100 -0.0000
## 500 0.4625 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0401
## 2 1.1674 nan 0.1000 0.0311
## 3 1.1199 nan 0.1000 0.0173
## 4 1.0669 nan 0.1000 0.0223
## 5 1.0269 nan 0.1000 0.0177
## 6 0.9941 nan 0.1000 0.0127
## 7 0.9601 nan 0.1000 0.0157
## 8 0.9312 nan 0.1000 0.0117
## 9 0.9063 nan 0.1000 0.0085
## 10 0.8880 nan 0.1000 0.0071
## 20 0.7333 nan 0.1000 0.0049
## 40 0.5907 nan 0.1000 -0.0005
## 60 0.5052 nan 0.1000 -0.0018
## 80 0.4351 nan 0.1000 -0.0003
## 100 0.3833 nan 0.1000 -0.0009
## 120 0.3429 nan 0.1000 -0.0010
## 140 0.3058 nan 0.1000 0.0004
## 160 0.2714 nan 0.1000 -0.0002
## 180 0.2432 nan 0.1000 -0.0002
## 200 0.2183 nan 0.1000 -0.0004
## 220 0.1952 nan 0.1000 -0.0000
## 240 0.1751 nan 0.1000 -0.0005
## 260 0.1574 nan 0.1000 0.0000
## 280 0.1413 nan 0.1000 -0.0002
## 300 0.1276 nan 0.1000 -0.0006
## 320 0.1161 nan 0.1000 -0.0002
## 340 0.1066 nan 0.1000 -0.0001
## 360 0.0988 nan 0.1000 0.0001
## 380 0.0902 nan 0.1000 -0.0001
## 400 0.0819 nan 0.1000 -0.0002
## 420 0.0752 nan 0.1000 -0.0001
## 440 0.0690 nan 0.1000 -0.0003
## 460 0.0632 nan 0.1000 0.0001
## 480 0.0582 nan 0.1000 -0.0003
## 500 0.0533 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2442 nan 0.1000 0.0357
## 2 1.1832 nan 0.1000 0.0264
## 3 1.1289 nan 0.1000 0.0244
## 4 1.0775 nan 0.1000 0.0240
## 5 1.0320 nan 0.1000 0.0196
## 6 0.9941 nan 0.1000 0.0171
## 7 0.9614 nan 0.1000 0.0127
## 8 0.9324 nan 0.1000 0.0104
## 9 0.9084 nan 0.1000 0.0091
## 10 0.8821 nan 0.1000 0.0080
## 20 0.7443 nan 0.1000 0.0013
## 40 0.6162 nan 0.1000 -0.0018
## 60 0.5226 nan 0.1000 -0.0034
## 80 0.4514 nan 0.1000 0.0002
## 100 0.3994 nan 0.1000 -0.0014
## 120 0.3545 nan 0.1000 -0.0001
## 140 0.3176 nan 0.1000 -0.0009
## 160 0.2854 nan 0.1000 -0.0002
## 180 0.2552 nan 0.1000 0.0000
## 200 0.2288 nan 0.1000 -0.0003
## 220 0.2069 nan 0.1000 -0.0009
## 240 0.1861 nan 0.1000 -0.0004
## 260 0.1709 nan 0.1000 -0.0012
## 280 0.1572 nan 0.1000 -0.0006
## 300 0.1414 nan 0.1000 -0.0002
## 320 0.1298 nan 0.1000 -0.0003
## 340 0.1190 nan 0.1000 -0.0004
## 360 0.1097 nan 0.1000 -0.0003
## 380 0.1011 nan 0.1000 -0.0002
## 400 0.0934 nan 0.1000 -0.0004
## 420 0.0857 nan 0.1000 -0.0002
## 440 0.0799 nan 0.1000 -0.0002
## 460 0.0730 nan 0.1000 0.0001
## 480 0.0673 nan 0.1000 -0.0003
## 500 0.0622 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0316
## 2 1.1806 nan 0.1000 0.0264
## 3 1.1271 nan 0.1000 0.0242
## 4 1.0771 nan 0.1000 0.0229
## 5 1.0351 nan 0.1000 0.0175
## 6 0.9959 nan 0.1000 0.0179
## 7 0.9596 nan 0.1000 0.0170
## 8 0.9322 nan 0.1000 0.0101
## 9 0.9097 nan 0.1000 0.0081
## 10 0.8848 nan 0.1000 0.0088
## 20 0.7486 nan 0.1000 0.0022
## 40 0.6187 nan 0.1000 -0.0015
## 60 0.5322 nan 0.1000 -0.0008
## 80 0.4675 nan 0.1000 0.0012
## 100 0.4131 nan 0.1000 -0.0002
## 120 0.3656 nan 0.1000 -0.0003
## 140 0.3288 nan 0.1000 -0.0008
## 160 0.2927 nan 0.1000 -0.0008
## 180 0.2652 nan 0.1000 -0.0009
## 200 0.2418 nan 0.1000 -0.0003
## 220 0.2200 nan 0.1000 -0.0005
## 240 0.2016 nan 0.1000 -0.0008
## 260 0.1860 nan 0.1000 -0.0013
## 280 0.1680 nan 0.1000 -0.0001
## 300 0.1547 nan 0.1000 -0.0006
## 320 0.1416 nan 0.1000 -0.0004
## 340 0.1306 nan 0.1000 -0.0003
## 360 0.1208 nan 0.1000 0.0003
## 380 0.1118 nan 0.1000 -0.0003
## 400 0.1030 nan 0.1000 -0.0003
## 420 0.0953 nan 0.1000 -0.0002
## 440 0.0872 nan 0.1000 0.0001
## 460 0.0798 nan 0.1000 -0.0002
## 480 0.0742 nan 0.1000 -0.0004
## 500 0.0688 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2284 nan 0.1000 0.0418
## 2 1.1607 nan 0.1000 0.0262
## 3 1.1056 nan 0.1000 0.0179
## 4 1.0527 nan 0.1000 0.0234
## 5 1.0120 nan 0.1000 0.0155
## 6 0.9715 nan 0.1000 0.0171
## 7 0.9378 nan 0.1000 0.0132
## 8 0.9013 nan 0.1000 0.0151
## 9 0.8768 nan 0.1000 0.0063
## 10 0.8572 nan 0.1000 0.0070
## 20 0.6932 nan 0.1000 0.0031
## 40 0.5408 nan 0.1000 0.0002
## 60 0.4604 nan 0.1000 -0.0014
## 80 0.3817 nan 0.1000 -0.0015
## 100 0.3252 nan 0.1000 0.0001
## 120 0.2802 nan 0.1000 -0.0008
## 140 0.2428 nan 0.1000 -0.0004
## 160 0.2098 nan 0.1000 -0.0006
## 180 0.1821 nan 0.1000 0.0002
## 200 0.1603 nan 0.1000 -0.0002
## 220 0.1396 nan 0.1000 0.0001
## 240 0.1226 nan 0.1000 -0.0005
## 260 0.1083 nan 0.1000 -0.0003
## 280 0.0966 nan 0.1000 -0.0003
## 300 0.0873 nan 0.1000 -0.0002
## 320 0.0789 nan 0.1000 -0.0002
## 340 0.0701 nan 0.1000 0.0001
## 360 0.0624 nan 0.1000 -0.0000
## 380 0.0561 nan 0.1000 -0.0001
## 400 0.0507 nan 0.1000 -0.0001
## 420 0.0452 nan 0.1000 -0.0000
## 440 0.0410 nan 0.1000 -0.0001
## 460 0.0367 nan 0.1000 -0.0001
## 480 0.0328 nan 0.1000 -0.0001
## 500 0.0298 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2302 nan 0.1000 0.0435
## 2 1.1650 nan 0.1000 0.0295
## 3 1.1131 nan 0.1000 0.0217
## 4 1.0603 nan 0.1000 0.0229
## 5 1.0176 nan 0.1000 0.0204
## 6 0.9806 nan 0.1000 0.0145
## 7 0.9426 nan 0.1000 0.0168
## 8 0.9123 nan 0.1000 0.0138
## 9 0.8847 nan 0.1000 0.0108
## 10 0.8552 nan 0.1000 0.0106
## 20 0.7055 nan 0.1000 0.0001
## 40 0.5540 nan 0.1000 0.0029
## 60 0.4641 nan 0.1000 -0.0013
## 80 0.3881 nan 0.1000 -0.0009
## 100 0.3293 nan 0.1000 -0.0007
## 120 0.2851 nan 0.1000 -0.0007
## 140 0.2461 nan 0.1000 -0.0011
## 160 0.2174 nan 0.1000 -0.0001
## 180 0.1863 nan 0.1000 -0.0008
## 200 0.1639 nan 0.1000 -0.0002
## 220 0.1451 nan 0.1000 -0.0003
## 240 0.1284 nan 0.1000 -0.0002
## 260 0.1148 nan 0.1000 -0.0002
## 280 0.1008 nan 0.1000 -0.0004
## 300 0.0905 nan 0.1000 -0.0001
## 320 0.0793 nan 0.1000 -0.0001
## 340 0.0708 nan 0.1000 -0.0002
## 360 0.0632 nan 0.1000 -0.0003
## 380 0.0559 nan 0.1000 -0.0001
## 400 0.0506 nan 0.1000 -0.0002
## 420 0.0461 nan 0.1000 -0.0002
## 440 0.0414 nan 0.1000 -0.0001
## 460 0.0374 nan 0.1000 -0.0001
## 480 0.0336 nan 0.1000 -0.0000
## 500 0.0303 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0360
## 2 1.1636 nan 0.1000 0.0357
## 3 1.1068 nan 0.1000 0.0253
## 4 1.0531 nan 0.1000 0.0252
## 5 1.0096 nan 0.1000 0.0190
## 6 0.9771 nan 0.1000 0.0142
## 7 0.9412 nan 0.1000 0.0121
## 8 0.9112 nan 0.1000 0.0128
## 9 0.8846 nan 0.1000 0.0091
## 10 0.8576 nan 0.1000 0.0107
## 20 0.7034 nan 0.1000 0.0048
## 40 0.5497 nan 0.1000 0.0007
## 60 0.4685 nan 0.1000 -0.0003
## 80 0.4007 nan 0.1000 -0.0011
## 100 0.3477 nan 0.1000 -0.0013
## 120 0.3014 nan 0.1000 -0.0003
## 140 0.2625 nan 0.1000 -0.0006
## 160 0.2328 nan 0.1000 -0.0005
## 180 0.2099 nan 0.1000 -0.0006
## 200 0.1850 nan 0.1000 -0.0009
## 220 0.1627 nan 0.1000 -0.0005
## 240 0.1452 nan 0.1000 -0.0002
## 260 0.1309 nan 0.1000 -0.0004
## 280 0.1170 nan 0.1000 -0.0004
## 300 0.1048 nan 0.1000 -0.0006
## 320 0.0939 nan 0.1000 -0.0004
## 340 0.0850 nan 0.1000 -0.0002
## 360 0.0764 nan 0.1000 -0.0002
## 380 0.0679 nan 0.1000 -0.0001
## 400 0.0606 nan 0.1000 -0.0003
## 420 0.0550 nan 0.1000 -0.0001
## 440 0.0500 nan 0.1000 -0.0002
## 460 0.0453 nan 0.1000 -0.0000
## 480 0.0405 nan 0.1000 -0.0002
## 500 0.0364 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0361
## 2 1.1540 nan 0.1000 0.0367
## 3 1.0851 nan 0.1000 0.0297
## 4 1.0349 nan 0.1000 0.0189
## 5 0.9886 nan 0.1000 0.0174
## 6 0.9506 nan 0.1000 0.0141
## 7 0.9125 nan 0.1000 0.0150
## 8 0.8775 nan 0.1000 0.0145
## 9 0.8477 nan 0.1000 0.0103
## 10 0.8224 nan 0.1000 0.0079
## 20 0.6541 nan 0.1000 -0.0011
## 40 0.4871 nan 0.1000 -0.0006
## 60 0.3957 nan 0.1000 -0.0014
## 80 0.3381 nan 0.1000 -0.0013
## 100 0.2759 nan 0.1000 -0.0001
## 120 0.2369 nan 0.1000 -0.0009
## 140 0.2025 nan 0.1000 -0.0003
## 160 0.1712 nan 0.1000 -0.0001
## 180 0.1478 nan 0.1000 -0.0004
## 200 0.1285 nan 0.1000 -0.0004
## 220 0.1099 nan 0.1000 -0.0002
## 240 0.0949 nan 0.1000 -0.0003
## 260 0.0823 nan 0.1000 -0.0004
## 280 0.0718 nan 0.1000 -0.0001
## 300 0.0634 nan 0.1000 -0.0002
## 320 0.0549 nan 0.1000 -0.0003
## 340 0.0485 nan 0.1000 -0.0000
## 360 0.0421 nan 0.1000 -0.0001
## 380 0.0374 nan 0.1000 -0.0001
## 400 0.0332 nan 0.1000 -0.0000
## 420 0.0294 nan 0.1000 -0.0000
## 440 0.0259 nan 0.1000 -0.0001
## 460 0.0229 nan 0.1000 -0.0001
## 480 0.0202 nan 0.1000 -0.0000
## 500 0.0180 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.1000 0.0378
## 2 1.1544 nan 0.1000 0.0309
## 3 1.0967 nan 0.1000 0.0214
## 4 1.0418 nan 0.1000 0.0235
## 5 0.9973 nan 0.1000 0.0172
## 6 0.9544 nan 0.1000 0.0162
## 7 0.9188 nan 0.1000 0.0141
## 8 0.8855 nan 0.1000 0.0121
## 9 0.8542 nan 0.1000 0.0111
## 10 0.8302 nan 0.1000 0.0092
## 20 0.6667 nan 0.1000 0.0027
## 40 0.5060 nan 0.1000 -0.0001
## 60 0.4009 nan 0.1000 0.0011
## 80 0.3305 nan 0.1000 -0.0001
## 100 0.2756 nan 0.1000 -0.0001
## 120 0.2333 nan 0.1000 -0.0005
## 140 0.1999 nan 0.1000 -0.0005
## 160 0.1697 nan 0.1000 -0.0001
## 180 0.1449 nan 0.1000 -0.0005
## 200 0.1228 nan 0.1000 -0.0003
## 220 0.1062 nan 0.1000 -0.0003
## 240 0.0917 nan 0.1000 -0.0002
## 260 0.0802 nan 0.1000 -0.0004
## 280 0.0704 nan 0.1000 -0.0003
## 300 0.0621 nan 0.1000 -0.0002
## 320 0.0538 nan 0.1000 -0.0002
## 340 0.0471 nan 0.1000 -0.0000
## 360 0.0413 nan 0.1000 -0.0001
## 380 0.0361 nan 0.1000 -0.0002
## 400 0.0317 nan 0.1000 -0.0000
## 420 0.0281 nan 0.1000 -0.0001
## 440 0.0245 nan 0.1000 -0.0000
## 460 0.0217 nan 0.1000 -0.0000
## 480 0.0195 nan 0.1000 -0.0001
## 500 0.0173 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0403
## 2 1.1658 nan 0.1000 0.0345
## 3 1.1071 nan 0.1000 0.0246
## 4 1.0520 nan 0.1000 0.0245
## 5 1.0014 nan 0.1000 0.0232
## 6 0.9603 nan 0.1000 0.0155
## 7 0.9261 nan 0.1000 0.0153
## 8 0.8930 nan 0.1000 0.0120
## 9 0.8614 nan 0.1000 0.0103
## 10 0.8401 nan 0.1000 0.0065
## 20 0.6902 nan 0.1000 0.0011
## 40 0.5086 nan 0.1000 0.0016
## 60 0.4168 nan 0.1000 -0.0002
## 80 0.3509 nan 0.1000 -0.0004
## 100 0.2943 nan 0.1000 -0.0005
## 120 0.2485 nan 0.1000 0.0003
## 140 0.2117 nan 0.1000 -0.0020
## 160 0.1805 nan 0.1000 -0.0006
## 180 0.1541 nan 0.1000 -0.0006
## 200 0.1340 nan 0.1000 -0.0007
## 220 0.1158 nan 0.1000 -0.0002
## 240 0.1021 nan 0.1000 -0.0003
## 260 0.0898 nan 0.1000 -0.0002
## 280 0.0790 nan 0.1000 -0.0007
## 300 0.0686 nan 0.1000 -0.0003
## 320 0.0608 nan 0.1000 -0.0000
## 340 0.0539 nan 0.1000 -0.0004
## 360 0.0472 nan 0.1000 -0.0003
## 380 0.0417 nan 0.1000 -0.0002
## 400 0.0368 nan 0.1000 -0.0001
## 420 0.0326 nan 0.1000 -0.0001
## 440 0.0288 nan 0.1000 -0.0001
## 460 0.0253 nan 0.1000 -0.0001
## 480 0.0221 nan 0.1000 -0.0000
## 500 0.0200 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0003
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2869 nan 0.0010 0.0004
## 60 1.2709 nan 0.0010 0.0004
## 80 1.2550 nan 0.0010 0.0004
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2257 nan 0.0010 0.0003
## 140 1.2118 nan 0.0010 0.0003
## 160 1.1979 nan 0.0010 0.0003
## 180 1.1846 nan 0.0010 0.0003
## 200 1.1713 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0002
## 240 1.1466 nan 0.0010 0.0003
## 260 1.1347 nan 0.0010 0.0003
## 280 1.1232 nan 0.0010 0.0003
## 300 1.1118 nan 0.0010 0.0002
## 320 1.1007 nan 0.0010 0.0002
## 340 1.0900 nan 0.0010 0.0002
## 360 1.0800 nan 0.0010 0.0002
## 380 1.0699 nan 0.0010 0.0002
## 400 1.0603 nan 0.0010 0.0002
## 420 1.0507 nan 0.0010 0.0002
## 440 1.0415 nan 0.0010 0.0002
## 460 1.0326 nan 0.0010 0.0002
## 480 1.0239 nan 0.0010 0.0002
## 500 1.0154 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3037 nan 0.0010 0.0004
## 40 1.2875 nan 0.0010 0.0004
## 60 1.2713 nan 0.0010 0.0003
## 80 1.2556 nan 0.0010 0.0004
## 100 1.2406 nan 0.0010 0.0003
## 120 1.2262 nan 0.0010 0.0003
## 140 1.2121 nan 0.0010 0.0003
## 160 1.1984 nan 0.0010 0.0003
## 180 1.1852 nan 0.0010 0.0003
## 200 1.1722 nan 0.0010 0.0003
## 220 1.1594 nan 0.0010 0.0003
## 240 1.1472 nan 0.0010 0.0003
## 260 1.1355 nan 0.0010 0.0003
## 280 1.1241 nan 0.0010 0.0002
## 300 1.1128 nan 0.0010 0.0003
## 320 1.1020 nan 0.0010 0.0002
## 340 1.0915 nan 0.0010 0.0003
## 360 1.0812 nan 0.0010 0.0002
## 380 1.0713 nan 0.0010 0.0002
## 400 1.0613 nan 0.0010 0.0002
## 420 1.0518 nan 0.0010 0.0002
## 440 1.0425 nan 0.0010 0.0002
## 460 1.0335 nan 0.0010 0.0002
## 480 1.0249 nan 0.0010 0.0002
## 500 1.0164 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0003
## 20 1.3038 nan 0.0010 0.0003
## 40 1.2872 nan 0.0010 0.0004
## 60 1.2714 nan 0.0010 0.0003
## 80 1.2560 nan 0.0010 0.0003
## 100 1.2414 nan 0.0010 0.0004
## 120 1.2269 nan 0.0010 0.0003
## 140 1.2131 nan 0.0010 0.0003
## 160 1.1995 nan 0.0010 0.0003
## 180 1.1860 nan 0.0010 0.0003
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## 320 1.1030 nan 0.0010 0.0002
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## 380 1.0726 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0533 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0351 nan 0.0010 0.0002
## 480 1.0264 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0005
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0003
## 80 1.2510 nan 0.0010 0.0003
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2189 nan 0.0010 0.0004
## 140 1.2037 nan 0.0010 0.0003
## 160 1.1891 nan 0.0010 0.0003
## 180 1.1749 nan 0.0010 0.0003
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## 240 1.1348 nan 0.0010 0.0003
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## 280 1.1101 nan 0.0010 0.0003
## 300 1.0982 nan 0.0010 0.0002
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0755 nan 0.0010 0.0003
## 360 1.0643 nan 0.0010 0.0002
## 380 1.0536 nan 0.0010 0.0002
## 400 1.0432 nan 0.0010 0.0002
## 420 1.0334 nan 0.0010 0.0002
## 440 1.0237 nan 0.0010 0.0002
## 460 1.0140 nan 0.0010 0.0002
## 480 1.0049 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3133 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0005
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3024 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2675 nan 0.0010 0.0004
## 80 1.2511 nan 0.0010 0.0004
## 100 1.2353 nan 0.0010 0.0004
## 120 1.2197 nan 0.0010 0.0003
## 140 1.2047 nan 0.0010 0.0003
## 160 1.1902 nan 0.0010 0.0003
## 180 1.1761 nan 0.0010 0.0003
## 200 1.1624 nan 0.0010 0.0003
## 220 1.1490 nan 0.0010 0.0003
## 240 1.1360 nan 0.0010 0.0003
## 260 1.1235 nan 0.0010 0.0002
## 280 1.1115 nan 0.0010 0.0003
## 300 1.0996 nan 0.0010 0.0003
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0773 nan 0.0010 0.0002
## 360 1.0663 nan 0.0010 0.0002
## 380 1.0561 nan 0.0010 0.0002
## 400 1.0455 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0256 nan 0.0010 0.0002
## 460 1.0160 nan 0.0010 0.0002
## 480 1.0068 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3025 nan 0.0010 0.0004
## 40 1.2853 nan 0.0010 0.0004
## 60 1.2682 nan 0.0010 0.0004
## 80 1.2518 nan 0.0010 0.0003
## 100 1.2358 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0004
## 140 1.2053 nan 0.0010 0.0003
## 160 1.1908 nan 0.0010 0.0003
## 180 1.1766 nan 0.0010 0.0003
## 200 1.1631 nan 0.0010 0.0003
## 220 1.1499 nan 0.0010 0.0003
## 240 1.1371 nan 0.0010 0.0003
## 260 1.1246 nan 0.0010 0.0003
## 280 1.1127 nan 0.0010 0.0003
## 300 1.1011 nan 0.0010 0.0002
## 320 1.0896 nan 0.0010 0.0003
## 340 1.0787 nan 0.0010 0.0002
## 360 1.0680 nan 0.0010 0.0002
## 380 1.0577 nan 0.0010 0.0002
## 400 1.0476 nan 0.0010 0.0002
## 420 1.0376 nan 0.0010 0.0002
## 440 1.0279 nan 0.0010 0.0001
## 460 1.0183 nan 0.0010 0.0002
## 480 1.0090 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0005
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2829 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0004
## 100 1.2299 nan 0.0010 0.0004
## 120 1.2132 nan 0.0010 0.0004
## 140 1.1977 nan 0.0010 0.0003
## 160 1.1825 nan 0.0010 0.0003
## 180 1.1679 nan 0.0010 0.0003
## 200 1.1535 nan 0.0010 0.0003
## 220 1.1395 nan 0.0010 0.0003
## 240 1.1262 nan 0.0010 0.0003
## 260 1.1128 nan 0.0010 0.0003
## 280 1.1000 nan 0.0010 0.0003
## 300 1.0876 nan 0.0010 0.0003
## 320 1.0757 nan 0.0010 0.0003
## 340 1.0641 nan 0.0010 0.0002
## 360 1.0526 nan 0.0010 0.0003
## 380 1.0416 nan 0.0010 0.0002
## 400 1.0308 nan 0.0010 0.0002
## 420 1.0203 nan 0.0010 0.0002
## 440 1.0101 nan 0.0010 0.0002
## 460 1.0000 nan 0.0010 0.0002
## 480 0.9901 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0004
## 10 1.3110 nan 0.0010 0.0005
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2476 nan 0.0010 0.0004
## 100 1.2311 nan 0.0010 0.0004
## 120 1.2150 nan 0.0010 0.0004
## 140 1.1992 nan 0.0010 0.0003
## 160 1.1839 nan 0.0010 0.0003
## 180 1.1692 nan 0.0010 0.0003
## 200 1.1545 nan 0.0010 0.0003
## 220 1.1408 nan 0.0010 0.0003
## 240 1.1274 nan 0.0010 0.0003
## 260 1.1144 nan 0.0010 0.0003
## 280 1.1015 nan 0.0010 0.0003
## 300 1.0894 nan 0.0010 0.0003
## 320 1.0773 nan 0.0010 0.0003
## 340 1.0658 nan 0.0010 0.0003
## 360 1.0545 nan 0.0010 0.0002
## 380 1.0436 nan 0.0010 0.0002
## 400 1.0331 nan 0.0010 0.0002
## 420 1.0227 nan 0.0010 0.0002
## 440 1.0127 nan 0.0010 0.0002
## 460 1.0028 nan 0.0010 0.0002
## 480 0.9931 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0004
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0004
## 10 1.3108 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2833 nan 0.0010 0.0005
## 60 1.2654 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0003
## 100 1.2323 nan 0.0010 0.0003
## 120 1.2165 nan 0.0010 0.0003
## 140 1.2008 nan 0.0010 0.0003
## 160 1.1856 nan 0.0010 0.0003
## 180 1.1710 nan 0.0010 0.0003
## 200 1.1569 nan 0.0010 0.0003
## 220 1.1432 nan 0.0010 0.0003
## 240 1.1300 nan 0.0010 0.0003
## 260 1.1169 nan 0.0010 0.0003
## 280 1.1044 nan 0.0010 0.0003
## 300 1.0919 nan 0.0010 0.0003
## 320 1.0800 nan 0.0010 0.0003
## 340 1.0686 nan 0.0010 0.0003
## 360 1.0575 nan 0.0010 0.0002
## 380 1.0467 nan 0.0010 0.0002
## 400 1.0361 nan 0.0010 0.0002
## 420 1.0259 nan 0.0010 0.0002
## 440 1.0159 nan 0.0010 0.0002
## 460 1.0061 nan 0.0010 0.0002
## 480 0.9966 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0041
## 2 1.3040 nan 0.0100 0.0038
## 3 1.2960 nan 0.0100 0.0042
## 4 1.2877 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0032
## 6 1.2716 nan 0.0100 0.0038
## 7 1.2630 nan 0.0100 0.0037
## 8 1.2550 nan 0.0100 0.0038
## 9 1.2470 nan 0.0100 0.0035
## 10 1.2392 nan 0.0100 0.0037
## 20 1.1720 nan 0.0100 0.0030
## 40 1.0611 nan 0.0100 0.0021
## 60 0.9745 nan 0.0100 0.0015
## 80 0.9067 nan 0.0100 0.0012
## 100 0.8507 nan 0.0100 0.0010
## 120 0.8060 nan 0.0100 0.0008
## 140 0.7694 nan 0.0100 0.0005
## 160 0.7387 nan 0.0100 0.0005
## 180 0.7128 nan 0.0100 0.0003
## 200 0.6898 nan 0.0100 0.0002
## 220 0.6696 nan 0.0100 0.0000
## 240 0.6510 nan 0.0100 0.0001
## 260 0.6348 nan 0.0100 0.0001
## 280 0.6196 nan 0.0100 -0.0001
## 300 0.6061 nan 0.0100 0.0001
## 320 0.5931 nan 0.0100 0.0001
## 340 0.5814 nan 0.0100 -0.0000
## 360 0.5700 nan 0.0100 -0.0002
## 380 0.5594 nan 0.0100 0.0001
## 400 0.5492 nan 0.0100 0.0000
## 420 0.5398 nan 0.0100 -0.0001
## 440 0.5310 nan 0.0100 -0.0000
## 460 0.5224 nan 0.0100 0.0000
## 480 0.5138 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2931 nan 0.0100 0.0038
## 4 1.2850 nan 0.0100 0.0035
## 5 1.2768 nan 0.0100 0.0036
## 6 1.2694 nan 0.0100 0.0033
## 7 1.2608 nan 0.0100 0.0040
## 8 1.2531 nan 0.0100 0.0037
## 9 1.2450 nan 0.0100 0.0037
## 10 1.2382 nan 0.0100 0.0032
## 20 1.1716 nan 0.0100 0.0026
## 40 1.0607 nan 0.0100 0.0021
## 60 0.9749 nan 0.0100 0.0017
## 80 0.9080 nan 0.0100 0.0011
## 100 0.8561 nan 0.0100 0.0010
## 120 0.8112 nan 0.0100 0.0008
## 140 0.7759 nan 0.0100 0.0004
## 160 0.7450 nan 0.0100 0.0002
## 180 0.7181 nan 0.0100 0.0003
## 200 0.6964 nan 0.0100 0.0004
## 220 0.6759 nan 0.0100 0.0001
## 240 0.6582 nan 0.0100 0.0001
## 260 0.6423 nan 0.0100 -0.0001
## 280 0.6277 nan 0.0100 0.0000
## 300 0.6136 nan 0.0100 0.0001
## 320 0.6017 nan 0.0100 0.0002
## 340 0.5903 nan 0.0100 -0.0002
## 360 0.5792 nan 0.0100 0.0000
## 380 0.5690 nan 0.0100 -0.0000
## 400 0.5589 nan 0.0100 -0.0001
## 420 0.5494 nan 0.0100 0.0000
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## 460 0.5330 nan 0.0100 -0.0000
## 480 0.5256 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0039
## 2 1.3037 nan 0.0100 0.0036
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2874 nan 0.0100 0.0039
## 5 1.2790 nan 0.0100 0.0037
## 6 1.2715 nan 0.0100 0.0036
## 7 1.2640 nan 0.0100 0.0034
## 8 1.2561 nan 0.0100 0.0034
## 9 1.2492 nan 0.0100 0.0030
## 10 1.2412 nan 0.0100 0.0038
## 20 1.1725 nan 0.0100 0.0025
## 40 1.0642 nan 0.0100 0.0023
## 60 0.9782 nan 0.0100 0.0017
## 80 0.9121 nan 0.0100 0.0010
## 100 0.8589 nan 0.0100 0.0011
## 120 0.8147 nan 0.0100 0.0005
## 140 0.7771 nan 0.0100 0.0007
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## 180 0.7204 nan 0.0100 0.0001
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## 320 0.6070 nan 0.0100 0.0000
## 340 0.5954 nan 0.0100 0.0001
## 360 0.5842 nan 0.0100 0.0000
## 380 0.5742 nan 0.0100 -0.0001
## 400 0.5648 nan 0.0100 -0.0000
## 420 0.5559 nan 0.0100 -0.0002
## 440 0.5474 nan 0.0100 -0.0002
## 460 0.5382 nan 0.0100 -0.0000
## 480 0.5312 nan 0.0100 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0045
## 2 1.3011 nan 0.0100 0.0045
## 3 1.2920 nan 0.0100 0.0041
## 4 1.2835 nan 0.0100 0.0037
## 5 1.2758 nan 0.0100 0.0035
## 6 1.2668 nan 0.0100 0.0037
## 7 1.2582 nan 0.0100 0.0038
## 8 1.2506 nan 0.0100 0.0038
## 9 1.2418 nan 0.0100 0.0042
## 10 1.2338 nan 0.0100 0.0034
## 20 1.1611 nan 0.0100 0.0029
## 40 1.0431 nan 0.0100 0.0022
## 60 0.9529 nan 0.0100 0.0017
## 80 0.8817 nan 0.0100 0.0012
## 100 0.8263 nan 0.0100 0.0009
## 120 0.7801 nan 0.0100 0.0008
## 140 0.7410 nan 0.0100 0.0006
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## 180 0.6812 nan 0.0100 0.0003
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## 240 0.6168 nan 0.0100 0.0000
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## 300 0.5678 nan 0.0100 0.0001
## 320 0.5549 nan 0.0100 -0.0000
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## 360 0.5307 nan 0.0100 -0.0001
## 380 0.5198 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0038
## 2 1.3031 nan 0.0100 0.0041
## 3 1.2941 nan 0.0100 0.0042
## 4 1.2859 nan 0.0100 0.0041
## 5 1.2767 nan 0.0100 0.0042
## 6 1.2685 nan 0.0100 0.0040
## 7 1.2606 nan 0.0100 0.0037
## 8 1.2520 nan 0.0100 0.0040
## 9 1.2433 nan 0.0100 0.0040
## 10 1.2356 nan 0.0100 0.0033
## 20 1.1621 nan 0.0100 0.0027
## 40 1.0441 nan 0.0100 0.0021
## 60 0.9532 nan 0.0100 0.0018
## 80 0.8826 nan 0.0100 0.0012
## 100 0.8248 nan 0.0100 0.0010
## 120 0.7784 nan 0.0100 0.0009
## 140 0.7399 nan 0.0100 0.0007
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## 180 0.6807 nan 0.0100 0.0003
## 200 0.6559 nan 0.0100 0.0002
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## 240 0.6163 nan 0.0100 0.0002
## 260 0.5987 nan 0.0100 0.0001
## 280 0.5835 nan 0.0100 -0.0002
## 300 0.5695 nan 0.0100 0.0001
## 320 0.5561 nan 0.0100 0.0001
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## 360 0.5330 nan 0.0100 -0.0000
## 380 0.5222 nan 0.0100 -0.0001
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## 480 0.4724 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3017 nan 0.0100 0.0044
## 3 1.2928 nan 0.0100 0.0043
## 4 1.2845 nan 0.0100 0.0037
## 5 1.2760 nan 0.0100 0.0040
## 6 1.2675 nan 0.0100 0.0041
## 7 1.2593 nan 0.0100 0.0037
## 8 1.2509 nan 0.0100 0.0038
## 9 1.2429 nan 0.0100 0.0034
## 10 1.2351 nan 0.0100 0.0035
## 20 1.1632 nan 0.0100 0.0030
## 40 1.0469 nan 0.0100 0.0022
## 60 0.9573 nan 0.0100 0.0018
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## 100 0.8332 nan 0.0100 0.0007
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## 140 0.7500 nan 0.0100 0.0004
## 160 0.7186 nan 0.0100 0.0003
## 180 0.6914 nan 0.0100 0.0002
## 200 0.6666 nan 0.0100 0.0004
## 220 0.6452 nan 0.0100 0.0002
## 240 0.6271 nan 0.0100 -0.0000
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## 280 0.5955 nan 0.0100 0.0000
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## 380 0.5321 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0039
## 2 1.3010 nan 0.0100 0.0048
## 3 1.2919 nan 0.0100 0.0043
## 4 1.2829 nan 0.0100 0.0041
## 5 1.2732 nan 0.0100 0.0044
## 6 1.2640 nan 0.0100 0.0041
## 7 1.2556 nan 0.0100 0.0039
## 8 1.2467 nan 0.0100 0.0041
## 9 1.2379 nan 0.0100 0.0036
## 10 1.2301 nan 0.0100 0.0035
## 20 1.1519 nan 0.0100 0.0030
## 40 1.0295 nan 0.0100 0.0024
## 60 0.9356 nan 0.0100 0.0015
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## 340 0.4998 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3107 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0037
## 3 1.2924 nan 0.0100 0.0041
## 4 1.2835 nan 0.0100 0.0036
## 5 1.2743 nan 0.0100 0.0041
## 6 1.2654 nan 0.0100 0.0039
## 7 1.2571 nan 0.0100 0.0039
## 8 1.2486 nan 0.0100 0.0040
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## 10 1.2321 nan 0.0100 0.0042
## 20 1.1569 nan 0.0100 0.0031
## 40 1.0343 nan 0.0100 0.0024
## 60 0.9400 nan 0.0100 0.0017
## 80 0.8684 nan 0.0100 0.0012
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## 140 0.7211 nan 0.0100 0.0004
## 160 0.6863 nan 0.0100 0.0006
## 180 0.6579 nan 0.0100 0.0004
## 200 0.6335 nan 0.0100 0.0005
## 220 0.6112 nan 0.0100 0.0000
## 240 0.5914 nan 0.0100 0.0001
## 260 0.5723 nan 0.0100 -0.0001
## 280 0.5559 nan 0.0100 0.0000
## 300 0.5411 nan 0.0100 0.0001
## 320 0.5277 nan 0.0100 -0.0001
## 340 0.5134 nan 0.0100 0.0002
## 360 0.5003 nan 0.0100 0.0001
## 380 0.4878 nan 0.0100 -0.0000
## 400 0.4761 nan 0.0100 -0.0001
## 420 0.4648 nan 0.0100 -0.0000
## 440 0.4541 nan 0.0100 0.0000
## 460 0.4437 nan 0.0100 -0.0000
## 480 0.4339 nan 0.0100 -0.0002
## 500 0.4233 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3109 nan 0.0100 0.0042
## 2 1.3012 nan 0.0100 0.0042
## 3 1.2916 nan 0.0100 0.0044
## 4 1.2821 nan 0.0100 0.0044
## 5 1.2737 nan 0.0100 0.0039
## 6 1.2651 nan 0.0100 0.0037
## 7 1.2561 nan 0.0100 0.0042
## 8 1.2475 nan 0.0100 0.0040
## 9 1.2398 nan 0.0100 0.0031
## 10 1.2314 nan 0.0100 0.0037
## 20 1.1553 nan 0.0100 0.0033
## 40 1.0331 nan 0.0100 0.0021
## 60 0.9403 nan 0.0100 0.0015
## 80 0.8679 nan 0.0100 0.0012
## 100 0.8103 nan 0.0100 0.0014
## 120 0.7644 nan 0.0100 0.0007
## 140 0.7260 nan 0.0100 0.0007
## 160 0.6930 nan 0.0100 0.0004
## 180 0.6654 nan 0.0100 0.0003
## 200 0.6408 nan 0.0100 0.0003
## 220 0.6188 nan 0.0100 0.0001
## 240 0.5986 nan 0.0100 0.0002
## 260 0.5809 nan 0.0100 0.0001
## 280 0.5645 nan 0.0100 0.0000
## 300 0.5497 nan 0.0100 0.0000
## 320 0.5347 nan 0.0100 -0.0000
## 340 0.5215 nan 0.0100 0.0000
## 360 0.5085 nan 0.0100 -0.0000
## 380 0.4955 nan 0.0100 -0.0000
## 400 0.4841 nan 0.0100 0.0001
## 420 0.4739 nan 0.0100 0.0000
## 440 0.4634 nan 0.0100 -0.0001
## 460 0.4534 nan 0.0100 0.0001
## 480 0.4435 nan 0.0100 -0.0001
## 500 0.4338 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2312 nan 0.1000 0.0359
## 2 1.1616 nan 0.1000 0.0330
## 3 1.1025 nan 0.1000 0.0257
## 4 1.0514 nan 0.1000 0.0209
## 5 1.0030 nan 0.1000 0.0187
## 6 0.9638 nan 0.1000 0.0154
## 7 0.9298 nan 0.1000 0.0110
## 8 0.9016 nan 0.1000 0.0104
## 9 0.8767 nan 0.1000 0.0104
## 10 0.8546 nan 0.1000 0.0083
## 20 0.6865 nan 0.1000 0.0038
## 40 0.5493 nan 0.1000 -0.0001
## 60 0.4713 nan 0.1000 -0.0011
## 80 0.4040 nan 0.1000 -0.0010
## 100 0.3571 nan 0.1000 0.0001
## 120 0.3154 nan 0.1000 -0.0002
## 140 0.2807 nan 0.1000 -0.0008
## 160 0.2489 nan 0.1000 -0.0004
## 180 0.2225 nan 0.1000 -0.0010
## 200 0.2002 nan 0.1000 -0.0002
## 220 0.1824 nan 0.1000 -0.0008
## 240 0.1638 nan 0.1000 -0.0003
## 260 0.1506 nan 0.1000 -0.0003
## 280 0.1381 nan 0.1000 -0.0003
## 300 0.1271 nan 0.1000 -0.0004
## 320 0.1159 nan 0.1000 -0.0001
## 340 0.1058 nan 0.1000 -0.0003
## 360 0.0964 nan 0.1000 -0.0001
## 380 0.0891 nan 0.1000 -0.0002
## 400 0.0819 nan 0.1000 -0.0002
## 420 0.0750 nan 0.1000 -0.0003
## 440 0.0690 nan 0.1000 0.0000
## 460 0.0642 nan 0.1000 -0.0002
## 480 0.0586 nan 0.1000 -0.0000
## 500 0.0540 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0405
## 2 1.1632 nan 0.1000 0.0292
## 3 1.1057 nan 0.1000 0.0259
## 4 1.0539 nan 0.1000 0.0229
## 5 1.0083 nan 0.1000 0.0192
## 6 0.9683 nan 0.1000 0.0170
## 7 0.9354 nan 0.1000 0.0132
## 8 0.9041 nan 0.1000 0.0154
## 9 0.8761 nan 0.1000 0.0123
## 10 0.8519 nan 0.1000 0.0100
## 20 0.6943 nan 0.1000 0.0011
## 40 0.5614 nan 0.1000 -0.0005
## 60 0.4755 nan 0.1000 -0.0018
## 80 0.4244 nan 0.1000 -0.0008
## 100 0.3799 nan 0.1000 -0.0000
## 120 0.3345 nan 0.1000 -0.0005
## 140 0.3027 nan 0.1000 -0.0013
## 160 0.2761 nan 0.1000 -0.0000
## 180 0.2458 nan 0.1000 -0.0008
## 200 0.2218 nan 0.1000 -0.0006
## 220 0.1996 nan 0.1000 -0.0007
## 240 0.1822 nan 0.1000 -0.0009
## 260 0.1657 nan 0.1000 -0.0006
## 280 0.1493 nan 0.1000 -0.0006
## 300 0.1356 nan 0.1000 -0.0003
## 320 0.1239 nan 0.1000 -0.0002
## 340 0.1137 nan 0.1000 -0.0002
## 360 0.1027 nan 0.1000 -0.0002
## 380 0.0939 nan 0.1000 -0.0001
## 400 0.0866 nan 0.1000 -0.0006
## 420 0.0789 nan 0.1000 0.0000
## 440 0.0727 nan 0.1000 -0.0002
## 460 0.0669 nan 0.1000 -0.0001
## 480 0.0610 nan 0.1000 -0.0002
## 500 0.0561 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2344 nan 0.1000 0.0380
## 2 1.1674 nan 0.1000 0.0285
## 3 1.1020 nan 0.1000 0.0275
## 4 1.0506 nan 0.1000 0.0207
## 5 1.0083 nan 0.1000 0.0179
## 6 0.9731 nan 0.1000 0.0143
## 7 0.9379 nan 0.1000 0.0150
## 8 0.9058 nan 0.1000 0.0131
## 9 0.8767 nan 0.1000 0.0123
## 10 0.8506 nan 0.1000 0.0111
## 20 0.6959 nan 0.1000 0.0036
## 40 0.5707 nan 0.1000 0.0002
## 60 0.4848 nan 0.1000 -0.0022
## 80 0.4217 nan 0.1000 0.0001
## 100 0.3742 nan 0.1000 -0.0011
## 120 0.3345 nan 0.1000 -0.0008
## 140 0.2986 nan 0.1000 -0.0013
## 160 0.2694 nan 0.1000 -0.0007
## 180 0.2411 nan 0.1000 -0.0008
## 200 0.2169 nan 0.1000 -0.0005
## 220 0.1989 nan 0.1000 -0.0007
## 240 0.1795 nan 0.1000 -0.0005
## 260 0.1645 nan 0.1000 -0.0008
## 280 0.1512 nan 0.1000 -0.0004
## 300 0.1394 nan 0.1000 -0.0004
## 320 0.1281 nan 0.1000 -0.0002
## 340 0.1164 nan 0.1000 -0.0003
## 360 0.1081 nan 0.1000 -0.0003
## 380 0.0993 nan 0.1000 -0.0002
## 400 0.0915 nan 0.1000 -0.0003
## 420 0.0849 nan 0.1000 -0.0004
## 440 0.0777 nan 0.1000 -0.0003
## 460 0.0710 nan 0.1000 -0.0003
## 480 0.0649 nan 0.1000 -0.0003
## 500 0.0601 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2299 nan 0.1000 0.0396
## 2 1.1525 nan 0.1000 0.0348
## 3 1.0850 nan 0.1000 0.0300
## 4 1.0360 nan 0.1000 0.0209
## 5 0.9886 nan 0.1000 0.0198
## 6 0.9493 nan 0.1000 0.0165
## 7 0.9070 nan 0.1000 0.0177
## 8 0.8741 nan 0.1000 0.0122
## 9 0.8447 nan 0.1000 0.0123
## 10 0.8192 nan 0.1000 0.0107
## 20 0.6511 nan 0.1000 0.0004
## 40 0.5095 nan 0.1000 0.0002
## 60 0.4149 nan 0.1000 -0.0012
## 80 0.3518 nan 0.1000 -0.0002
## 100 0.2978 nan 0.1000 -0.0001
## 120 0.2555 nan 0.1000 -0.0007
## 140 0.2213 nan 0.1000 -0.0004
## 160 0.1963 nan 0.1000 -0.0006
## 180 0.1716 nan 0.1000 -0.0005
## 200 0.1509 nan 0.1000 -0.0003
## 220 0.1337 nan 0.1000 -0.0005
## 240 0.1186 nan 0.1000 -0.0006
## 260 0.1052 nan 0.1000 -0.0003
## 280 0.0931 nan 0.1000 -0.0001
## 300 0.0844 nan 0.1000 -0.0004
## 320 0.0762 nan 0.1000 -0.0001
## 340 0.0689 nan 0.1000 -0.0002
## 360 0.0619 nan 0.1000 0.0000
## 380 0.0560 nan 0.1000 -0.0001
## 400 0.0504 nan 0.1000 -0.0002
## 420 0.0455 nan 0.1000 -0.0002
## 440 0.0410 nan 0.1000 -0.0000
## 460 0.0369 nan 0.1000 -0.0001
## 480 0.0330 nan 0.1000 -0.0001
## 500 0.0298 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2326 nan 0.1000 0.0396
## 2 1.1628 nan 0.1000 0.0312
## 3 1.0985 nan 0.1000 0.0313
## 4 1.0416 nan 0.1000 0.0242
## 5 0.9933 nan 0.1000 0.0188
## 6 0.9508 nan 0.1000 0.0144
## 7 0.9147 nan 0.1000 0.0153
## 8 0.8787 nan 0.1000 0.0119
## 9 0.8490 nan 0.1000 0.0124
## 10 0.8245 nan 0.1000 0.0092
## 20 0.6641 nan 0.1000 0.0010
## 40 0.5280 nan 0.1000 0.0003
## 60 0.4445 nan 0.1000 -0.0006
## 80 0.3750 nan 0.1000 -0.0005
## 100 0.3252 nan 0.1000 -0.0010
## 120 0.2829 nan 0.1000 -0.0014
## 140 0.2435 nan 0.1000 -0.0003
## 160 0.2132 nan 0.1000 -0.0007
## 180 0.1834 nan 0.1000 0.0002
## 200 0.1632 nan 0.1000 -0.0004
## 220 0.1431 nan 0.1000 -0.0005
## 240 0.1249 nan 0.1000 0.0001
## 260 0.1110 nan 0.1000 -0.0003
## 280 0.0978 nan 0.1000 -0.0000
## 300 0.0866 nan 0.1000 -0.0003
## 320 0.0767 nan 0.1000 -0.0004
## 340 0.0689 nan 0.1000 -0.0002
## 360 0.0620 nan 0.1000 -0.0001
## 380 0.0552 nan 0.1000 -0.0004
## 400 0.0492 nan 0.1000 -0.0001
## 420 0.0448 nan 0.1000 -0.0001
## 440 0.0406 nan 0.1000 -0.0002
## 460 0.0368 nan 0.1000 -0.0000
## 480 0.0330 nan 0.1000 -0.0001
## 500 0.0299 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2302 nan 0.1000 0.0405
## 2 1.1556 nan 0.1000 0.0322
## 3 1.1020 nan 0.1000 0.0221
## 4 1.0469 nan 0.1000 0.0226
## 5 0.9985 nan 0.1000 0.0214
## 6 0.9571 nan 0.1000 0.0186
## 7 0.9200 nan 0.1000 0.0162
## 8 0.8891 nan 0.1000 0.0132
## 9 0.8587 nan 0.1000 0.0127
## 10 0.8375 nan 0.1000 0.0068
## 20 0.6701 nan 0.1000 0.0011
## 40 0.5279 nan 0.1000 -0.0005
## 60 0.4385 nan 0.1000 -0.0003
## 80 0.3770 nan 0.1000 -0.0006
## 100 0.3217 nan 0.1000 -0.0009
## 120 0.2817 nan 0.1000 -0.0002
## 140 0.2435 nan 0.1000 -0.0008
## 160 0.2131 nan 0.1000 -0.0012
## 180 0.1872 nan 0.1000 -0.0006
## 200 0.1672 nan 0.1000 -0.0008
## 220 0.1491 nan 0.1000 -0.0001
## 240 0.1315 nan 0.1000 -0.0006
## 260 0.1182 nan 0.1000 -0.0002
## 280 0.1060 nan 0.1000 -0.0005
## 300 0.0950 nan 0.1000 -0.0004
## 320 0.0856 nan 0.1000 -0.0004
## 340 0.0752 nan 0.1000 -0.0004
## 360 0.0672 nan 0.1000 -0.0002
## 380 0.0599 nan 0.1000 -0.0002
## 400 0.0542 nan 0.1000 -0.0003
## 420 0.0479 nan 0.1000 -0.0002
## 440 0.0432 nan 0.1000 -0.0002
## 460 0.0395 nan 0.1000 -0.0001
## 480 0.0356 nan 0.1000 -0.0002
## 500 0.0316 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2220 nan 0.1000 0.0449
## 2 1.1456 nan 0.1000 0.0328
## 3 1.0769 nan 0.1000 0.0298
## 4 1.0228 nan 0.1000 0.0230
## 5 0.9729 nan 0.1000 0.0230
## 6 0.9325 nan 0.1000 0.0163
## 7 0.8926 nan 0.1000 0.0167
## 8 0.8598 nan 0.1000 0.0101
## 9 0.8331 nan 0.1000 0.0109
## 10 0.8079 nan 0.1000 0.0086
## 20 0.6202 nan 0.1000 0.0021
## 40 0.4685 nan 0.1000 0.0001
## 60 0.3791 nan 0.1000 -0.0002
## 80 0.3083 nan 0.1000 -0.0005
## 100 0.2637 nan 0.1000 -0.0003
## 120 0.2179 nan 0.1000 -0.0008
## 140 0.1807 nan 0.1000 0.0001
## 160 0.1542 nan 0.1000 -0.0004
## 180 0.1333 nan 0.1000 -0.0005
## 200 0.1135 nan 0.1000 -0.0004
## 220 0.0971 nan 0.1000 -0.0002
## 240 0.0845 nan 0.1000 -0.0001
## 260 0.0732 nan 0.1000 -0.0000
## 280 0.0635 nan 0.1000 -0.0001
## 300 0.0547 nan 0.1000 -0.0000
## 320 0.0485 nan 0.1000 0.0000
## 340 0.0431 nan 0.1000 -0.0001
## 360 0.0377 nan 0.1000 -0.0001
## 380 0.0332 nan 0.1000 -0.0001
## 400 0.0294 nan 0.1000 -0.0001
## 420 0.0262 nan 0.1000 -0.0001
## 440 0.0236 nan 0.1000 -0.0001
## 460 0.0208 nan 0.1000 -0.0001
## 480 0.0186 nan 0.1000 -0.0001
## 500 0.0163 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2176 nan 0.1000 0.0449
## 2 1.1470 nan 0.1000 0.0321
## 3 1.0800 nan 0.1000 0.0275
## 4 1.0209 nan 0.1000 0.0240
## 5 0.9776 nan 0.1000 0.0180
## 6 0.9315 nan 0.1000 0.0182
## 7 0.8931 nan 0.1000 0.0151
## 8 0.8607 nan 0.1000 0.0114
## 9 0.8308 nan 0.1000 0.0123
## 10 0.8094 nan 0.1000 0.0081
## 20 0.6460 nan 0.1000 0.0017
## 40 0.4961 nan 0.1000 -0.0005
## 60 0.4043 nan 0.1000 -0.0017
## 80 0.3311 nan 0.1000 -0.0006
## 100 0.2779 nan 0.1000 -0.0003
## 120 0.2326 nan 0.1000 -0.0004
## 140 0.2003 nan 0.1000 -0.0004
## 160 0.1700 nan 0.1000 -0.0003
## 180 0.1423 nan 0.1000 -0.0003
## 200 0.1245 nan 0.1000 -0.0002
## 220 0.1074 nan 0.1000 -0.0001
## 240 0.0913 nan 0.1000 -0.0003
## 260 0.0798 nan 0.1000 -0.0005
## 280 0.0696 nan 0.1000 -0.0001
## 300 0.0601 nan 0.1000 -0.0001
## 320 0.0531 nan 0.1000 -0.0003
## 340 0.0465 nan 0.1000 -0.0003
## 360 0.0407 nan 0.1000 -0.0000
## 380 0.0359 nan 0.1000 -0.0001
## 400 0.0311 nan 0.1000 -0.0000
## 420 0.0278 nan 0.1000 -0.0001
## 440 0.0246 nan 0.1000 -0.0001
## 460 0.0215 nan 0.1000 -0.0000
## 480 0.0191 nan 0.1000 -0.0001
## 500 0.0167 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0394
## 2 1.1624 nan 0.1000 0.0359
## 3 1.0991 nan 0.1000 0.0315
## 4 1.0418 nan 0.1000 0.0226
## 5 0.9901 nan 0.1000 0.0228
## 6 0.9494 nan 0.1000 0.0177
## 7 0.9152 nan 0.1000 0.0138
## 8 0.8819 nan 0.1000 0.0136
## 9 0.8503 nan 0.1000 0.0113
## 10 0.8236 nan 0.1000 0.0109
## 20 0.6410 nan 0.1000 0.0018
## 40 0.4907 nan 0.1000 -0.0004
## 60 0.3960 nan 0.1000 -0.0019
## 80 0.3252 nan 0.1000 -0.0007
## 100 0.2745 nan 0.1000 -0.0008
## 120 0.2306 nan 0.1000 0.0002
## 140 0.1956 nan 0.1000 -0.0003
## 160 0.1687 nan 0.1000 -0.0002
## 180 0.1461 nan 0.1000 -0.0009
## 200 0.1275 nan 0.1000 -0.0009
## 220 0.1099 nan 0.1000 -0.0002
## 240 0.0967 nan 0.1000 -0.0006
## 260 0.0842 nan 0.1000 -0.0003
## 280 0.0726 nan 0.1000 -0.0003
## 300 0.0632 nan 0.1000 -0.0004
## 320 0.0555 nan 0.1000 -0.0000
## 340 0.0489 nan 0.1000 -0.0003
## 360 0.0429 nan 0.1000 -0.0002
## 380 0.0380 nan 0.1000 -0.0001
## 400 0.0332 nan 0.1000 -0.0002
## 420 0.0291 nan 0.1000 -0.0001
## 440 0.0258 nan 0.1000 -0.0002
## 460 0.0231 nan 0.1000 -0.0001
## 480 0.0206 nan 0.1000 -0.0001
## 500 0.0183 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0003
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3137 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0005
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2868 nan 0.0010 0.0003
## 60 1.2709 nan 0.0010 0.0003
## 80 1.2556 nan 0.0010 0.0004
## 100 1.2410 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2123 nan 0.0010 0.0003
## 160 1.1986 nan 0.0010 0.0003
## 180 1.1853 nan 0.0010 0.0003
## 200 1.1724 nan 0.0010 0.0003
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1475 nan 0.0010 0.0003
## 260 1.1353 nan 0.0010 0.0003
## 280 1.1237 nan 0.0010 0.0002
## 300 1.1124 nan 0.0010 0.0003
## 320 1.1013 nan 0.0010 0.0002
## 340 1.0908 nan 0.0010 0.0002
## 360 1.0806 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0607 nan 0.0010 0.0002
## 420 1.0512 nan 0.0010 0.0002
## 440 1.0422 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
## 500 1.0158 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3034 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2707 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0004
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2112 nan 0.0010 0.0003
## 160 1.1974 nan 0.0010 0.0003
## 180 1.1842 nan 0.0010 0.0003
## 200 1.1713 nan 0.0010 0.0003
## 220 1.1589 nan 0.0010 0.0003
## 240 1.1465 nan 0.0010 0.0003
## 260 1.1348 nan 0.0010 0.0003
## 280 1.1232 nan 0.0010 0.0003
## 300 1.1122 nan 0.0010 0.0002
## 320 1.1013 nan 0.0010 0.0002
## 340 1.0909 nan 0.0010 0.0002
## 360 1.0805 nan 0.0010 0.0002
## 380 1.0705 nan 0.0010 0.0002
## 400 1.0609 nan 0.0010 0.0002
## 420 1.0514 nan 0.0010 0.0002
## 440 1.0420 nan 0.0010 0.0002
## 460 1.0331 nan 0.0010 0.0002
## 480 1.0243 nan 0.0010 0.0002
## 500 1.0159 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0003
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0003
## 9 1.3128 nan 0.0010 0.0003
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2870 nan 0.0010 0.0004
## 60 1.2712 nan 0.0010 0.0003
## 80 1.2559 nan 0.0010 0.0004
## 100 1.2409 nan 0.0010 0.0003
## 120 1.2265 nan 0.0010 0.0003
## 140 1.2124 nan 0.0010 0.0003
## 160 1.1989 nan 0.0010 0.0003
## 180 1.1858 nan 0.0010 0.0003
## 200 1.1728 nan 0.0010 0.0003
## 220 1.1602 nan 0.0010 0.0003
## 240 1.1481 nan 0.0010 0.0002
## 260 1.1366 nan 0.0010 0.0003
## 280 1.1252 nan 0.0010 0.0002
## 300 1.1141 nan 0.0010 0.0002
## 320 1.1033 nan 0.0010 0.0002
## 340 1.0927 nan 0.0010 0.0003
## 360 1.0826 nan 0.0010 0.0002
## 380 1.0724 nan 0.0010 0.0002
## 400 1.0628 nan 0.0010 0.0002
## 420 1.0533 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0351 nan 0.0010 0.0002
## 480 1.0266 nan 0.0010 0.0002
## 500 1.0180 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3167 nan 0.0010 0.0005
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3120 nan 0.0010 0.0005
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3022 nan 0.0010 0.0004
## 40 1.2844 nan 0.0010 0.0004
## 60 1.2671 nan 0.0010 0.0004
## 80 1.2505 nan 0.0010 0.0003
## 100 1.2348 nan 0.0010 0.0003
## 120 1.2192 nan 0.0010 0.0003
## 140 1.2043 nan 0.0010 0.0003
## 160 1.1895 nan 0.0010 0.0003
## 180 1.1753 nan 0.0010 0.0003
## 200 1.1616 nan 0.0010 0.0003
## 220 1.1482 nan 0.0010 0.0003
## 240 1.1352 nan 0.0010 0.0003
## 260 1.1225 nan 0.0010 0.0003
## 280 1.1103 nan 0.0010 0.0003
## 300 1.0985 nan 0.0010 0.0003
## 320 1.0872 nan 0.0010 0.0003
## 340 1.0760 nan 0.0010 0.0003
## 360 1.0650 nan 0.0010 0.0002
## 380 1.0545 nan 0.0010 0.0002
## 400 1.0442 nan 0.0010 0.0002
## 420 1.0344 nan 0.0010 0.0002
## 440 1.0244 nan 0.0010 0.0002
## 460 1.0147 nan 0.0010 0.0002
## 480 1.0054 nan 0.0010 0.0002
## 500 0.9964 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0005
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2847 nan 0.0010 0.0003
## 60 1.2678 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0003
## 100 1.2352 nan 0.0010 0.0003
## 120 1.2192 nan 0.0010 0.0003
## 140 1.2042 nan 0.0010 0.0004
## 160 1.1898 nan 0.0010 0.0003
## 180 1.1758 nan 0.0010 0.0003
## 200 1.1619 nan 0.0010 0.0003
## 220 1.1487 nan 0.0010 0.0003
## 240 1.1357 nan 0.0010 0.0003
## 260 1.1232 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0994 nan 0.0010 0.0002
## 320 1.0880 nan 0.0010 0.0003
## 340 1.0769 nan 0.0010 0.0002
## 360 1.0660 nan 0.0010 0.0002
## 380 1.0556 nan 0.0010 0.0003
## 400 1.0453 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0254 nan 0.0010 0.0002
## 460 1.0159 nan 0.0010 0.0002
## 480 1.0065 nan 0.0010 0.0002
## 500 0.9976 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0005
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2516 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2200 nan 0.0010 0.0003
## 140 1.2050 nan 0.0010 0.0003
## 160 1.1905 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
## 200 1.1632 nan 0.0010 0.0003
## 220 1.1500 nan 0.0010 0.0003
## 240 1.1368 nan 0.0010 0.0003
## 260 1.1246 nan 0.0010 0.0003
## 280 1.1125 nan 0.0010 0.0002
## 300 1.1010 nan 0.0010 0.0002
## 320 1.0898 nan 0.0010 0.0003
## 340 1.0790 nan 0.0010 0.0002
## 360 1.0682 nan 0.0010 0.0002
## 380 1.0578 nan 0.0010 0.0002
## 400 1.0479 nan 0.0010 0.0002
## 420 1.0381 nan 0.0010 0.0002
## 440 1.0284 nan 0.0010 0.0002
## 460 1.0191 nan 0.0010 0.0002
## 480 1.0102 nan 0.0010 0.0002
## 500 1.0012 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3196 nan 0.0010 0.0005
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3176 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0005
## 8 1.3128 nan 0.0010 0.0005
## 9 1.3118 nan 0.0010 0.0004
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2648 nan 0.0010 0.0004
## 80 1.2474 nan 0.0010 0.0004
## 100 1.2304 nan 0.0010 0.0004
## 120 1.2138 nan 0.0010 0.0004
## 140 1.1979 nan 0.0010 0.0004
## 160 1.1823 nan 0.0010 0.0004
## 180 1.1677 nan 0.0010 0.0003
## 200 1.1531 nan 0.0010 0.0003
## 220 1.1393 nan 0.0010 0.0003
## 240 1.1260 nan 0.0010 0.0003
## 260 1.1130 nan 0.0010 0.0003
## 280 1.1001 nan 0.0010 0.0003
## 300 1.0877 nan 0.0010 0.0003
## 320 1.0756 nan 0.0010 0.0002
## 340 1.0640 nan 0.0010 0.0003
## 360 1.0523 nan 0.0010 0.0003
## 380 1.0413 nan 0.0010 0.0002
## 400 1.0304 nan 0.0010 0.0002
## 420 1.0199 nan 0.0010 0.0002
## 440 1.0098 nan 0.0010 0.0002
## 460 0.9997 nan 0.0010 0.0002
## 480 0.9898 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0005
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3148 nan 0.0010 0.0005
## 7 1.3139 nan 0.0010 0.0004
## 8 1.3130 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3111 nan 0.0010 0.0004
## 20 1.3015 nan 0.0010 0.0004
## 40 1.2828 nan 0.0010 0.0004
## 60 1.2650 nan 0.0010 0.0004
## 80 1.2478 nan 0.0010 0.0003
## 100 1.2309 nan 0.0010 0.0004
## 120 1.2148 nan 0.0010 0.0003
## 140 1.1993 nan 0.0010 0.0003
## 160 1.1836 nan 0.0010 0.0003
## 180 1.1690 nan 0.0010 0.0003
## 200 1.1548 nan 0.0010 0.0003
## 220 1.1411 nan 0.0010 0.0003
## 240 1.1276 nan 0.0010 0.0003
## 260 1.1147 nan 0.0010 0.0003
## 280 1.1021 nan 0.0010 0.0003
## 300 1.0896 nan 0.0010 0.0003
## 320 1.0772 nan 0.0010 0.0002
## 340 1.0656 nan 0.0010 0.0002
## 360 1.0544 nan 0.0010 0.0002
## 380 1.0434 nan 0.0010 0.0002
## 400 1.0328 nan 0.0010 0.0002
## 420 1.0226 nan 0.0010 0.0002
## 440 1.0123 nan 0.0010 0.0002
## 460 1.0024 nan 0.0010 0.0002
## 480 0.9927 nan 0.0010 0.0002
## 500 0.9832 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3157 nan 0.0010 0.0004
## 6 1.3147 nan 0.0010 0.0005
## 7 1.3138 nan 0.0010 0.0004
## 8 1.3129 nan 0.0010 0.0004
## 9 1.3119 nan 0.0010 0.0005
## 10 1.3109 nan 0.0010 0.0004
## 20 1.3013 nan 0.0010 0.0004
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2657 nan 0.0010 0.0004
## 80 1.2484 nan 0.0010 0.0004
## 100 1.2321 nan 0.0010 0.0004
## 120 1.2161 nan 0.0010 0.0004
## 140 1.2007 nan 0.0010 0.0003
## 160 1.1858 nan 0.0010 0.0003
## 180 1.1711 nan 0.0010 0.0003
## 200 1.1572 nan 0.0010 0.0003
## 220 1.1435 nan 0.0010 0.0003
## 240 1.1303 nan 0.0010 0.0003
## 260 1.1174 nan 0.0010 0.0003
## 280 1.1051 nan 0.0010 0.0002
## 300 1.0930 nan 0.0010 0.0003
## 320 1.0814 nan 0.0010 0.0002
## 340 1.0697 nan 0.0010 0.0003
## 360 1.0586 nan 0.0010 0.0002
## 380 1.0479 nan 0.0010 0.0002
## 400 1.0373 nan 0.0010 0.0002
## 420 1.0274 nan 0.0010 0.0002
## 440 1.0172 nan 0.0010 0.0002
## 460 1.0075 nan 0.0010 0.0002
## 480 0.9981 nan 0.0010 0.0002
## 500 0.9890 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0035
## 2 1.3041 nan 0.0100 0.0040
## 3 1.2960 nan 0.0100 0.0035
## 4 1.2883 nan 0.0100 0.0031
## 5 1.2802 nan 0.0100 0.0038
## 6 1.2715 nan 0.0100 0.0035
## 7 1.2636 nan 0.0100 0.0035
## 8 1.2552 nan 0.0100 0.0035
## 9 1.2476 nan 0.0100 0.0033
## 10 1.2399 nan 0.0100 0.0037
## 20 1.1685 nan 0.0100 0.0031
## 40 1.0582 nan 0.0100 0.0024
## 60 0.9744 nan 0.0100 0.0016
## 80 0.9076 nan 0.0100 0.0011
## 100 0.8542 nan 0.0100 0.0010
## 120 0.8104 nan 0.0100 0.0006
## 140 0.7743 nan 0.0100 0.0006
## 160 0.7446 nan 0.0100 0.0004
## 180 0.7176 nan 0.0100 0.0002
## 200 0.6940 nan 0.0100 0.0003
## 220 0.6731 nan 0.0100 0.0003
## 240 0.6559 nan 0.0100 0.0002
## 260 0.6391 nan 0.0100 0.0002
## 280 0.6233 nan 0.0100 0.0000
## 300 0.6093 nan 0.0100 0.0000
## 320 0.5974 nan 0.0100 0.0002
## 340 0.5841 nan 0.0100 0.0001
## 360 0.5734 nan 0.0100 0.0002
## 380 0.5624 nan 0.0100 -0.0001
## 400 0.5524 nan 0.0100 -0.0001
## 420 0.5432 nan 0.0100 -0.0002
## 440 0.5348 nan 0.0100 -0.0000
## 460 0.5267 nan 0.0100 -0.0000
## 480 0.5180 nan 0.0100 -0.0000
## 500 0.5097 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0041
## 2 1.3035 nan 0.0100 0.0039
## 3 1.2949 nan 0.0100 0.0039
## 4 1.2860 nan 0.0100 0.0040
## 5 1.2791 nan 0.0100 0.0034
## 6 1.2707 nan 0.0100 0.0037
## 7 1.2632 nan 0.0100 0.0035
## 8 1.2561 nan 0.0100 0.0031
## 9 1.2492 nan 0.0100 0.0032
## 10 1.2419 nan 0.0100 0.0034
## 20 1.1738 nan 0.0100 0.0030
## 40 1.0623 nan 0.0100 0.0021
## 60 0.9771 nan 0.0100 0.0018
## 80 0.9114 nan 0.0100 0.0011
## 100 0.8583 nan 0.0100 0.0009
## 120 0.8139 nan 0.0100 0.0006
## 140 0.7772 nan 0.0100 0.0006
## 160 0.7470 nan 0.0100 0.0003
## 180 0.7216 nan 0.0100 0.0004
## 200 0.6986 nan 0.0100 0.0002
## 220 0.6792 nan 0.0100 0.0004
## 240 0.6609 nan 0.0100 0.0002
## 260 0.6458 nan 0.0100 0.0002
## 280 0.6304 nan 0.0100 -0.0001
## 300 0.6169 nan 0.0100 0.0001
## 320 0.6033 nan 0.0100 0.0001
## 340 0.5910 nan 0.0100 0.0000
## 360 0.5800 nan 0.0100 -0.0001
## 380 0.5697 nan 0.0100 -0.0000
## 400 0.5594 nan 0.0100 -0.0001
## 420 0.5498 nan 0.0100 -0.0001
## 440 0.5402 nan 0.0100 -0.0001
## 460 0.5307 nan 0.0100 0.0001
## 480 0.5226 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0040
## 2 1.3030 nan 0.0100 0.0037
## 3 1.2947 nan 0.0100 0.0042
## 4 1.2870 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0036
## 6 1.2711 nan 0.0100 0.0032
## 7 1.2635 nan 0.0100 0.0034
## 8 1.2552 nan 0.0100 0.0040
## 9 1.2482 nan 0.0100 0.0031
## 10 1.2406 nan 0.0100 0.0035
## 20 1.1719 nan 0.0100 0.0026
## 40 1.0614 nan 0.0100 0.0019
## 60 0.9790 nan 0.0100 0.0013
## 80 0.9124 nan 0.0100 0.0011
## 100 0.8585 nan 0.0100 0.0009
## 120 0.8145 nan 0.0100 0.0006
## 140 0.7778 nan 0.0100 0.0006
## 160 0.7481 nan 0.0100 0.0005
## 180 0.7227 nan 0.0100 0.0001
## 200 0.7005 nan 0.0100 0.0003
## 220 0.6810 nan 0.0100 0.0000
## 240 0.6631 nan 0.0100 0.0002
## 260 0.6483 nan 0.0100 0.0000
## 280 0.6337 nan 0.0100 0.0002
## 300 0.6198 nan 0.0100 0.0000
## 320 0.6089 nan 0.0100 0.0001
## 340 0.5970 nan 0.0100 -0.0002
## 360 0.5860 nan 0.0100 0.0001
## 380 0.5754 nan 0.0100 -0.0001
## 400 0.5654 nan 0.0100 -0.0000
## 420 0.5562 nan 0.0100 -0.0001
## 440 0.5467 nan 0.0100 -0.0000
## 460 0.5382 nan 0.0100 -0.0002
## 480 0.5301 nan 0.0100 -0.0000
## 500 0.5211 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0038
## 2 1.3033 nan 0.0100 0.0040
## 3 1.2943 nan 0.0100 0.0040
## 4 1.2859 nan 0.0100 0.0031
## 5 1.2770 nan 0.0100 0.0037
## 6 1.2688 nan 0.0100 0.0035
## 7 1.2599 nan 0.0100 0.0037
## 8 1.2526 nan 0.0100 0.0034
## 9 1.2444 nan 0.0100 0.0034
## 10 1.2364 nan 0.0100 0.0032
## 20 1.1644 nan 0.0100 0.0032
## 40 1.0444 nan 0.0100 0.0021
## 60 0.9515 nan 0.0100 0.0019
## 80 0.8801 nan 0.0100 0.0013
## 100 0.8239 nan 0.0100 0.0010
## 120 0.7780 nan 0.0100 0.0008
## 140 0.7400 nan 0.0100 0.0004
## 160 0.7080 nan 0.0100 0.0004
## 180 0.6802 nan 0.0100 0.0003
## 200 0.6561 nan 0.0100 0.0001
## 220 0.6334 nan 0.0100 0.0002
## 240 0.6133 nan 0.0100 -0.0000
## 260 0.5954 nan 0.0100 0.0001
## 280 0.5805 nan 0.0100 0.0001
## 300 0.5648 nan 0.0100 0.0001
## 320 0.5510 nan 0.0100 0.0001
## 340 0.5388 nan 0.0100 -0.0001
## 360 0.5264 nan 0.0100 0.0001
## 380 0.5155 nan 0.0100 0.0001
## 400 0.5043 nan 0.0100 0.0000
## 420 0.4933 nan 0.0100 0.0002
## 440 0.4836 nan 0.0100 -0.0000
## 460 0.4739 nan 0.0100 0.0001
## 480 0.4654 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0042
## 2 1.3023 nan 0.0100 0.0035
## 3 1.2943 nan 0.0100 0.0034
## 4 1.2859 nan 0.0100 0.0037
## 5 1.2772 nan 0.0100 0.0038
## 6 1.2683 nan 0.0100 0.0041
## 7 1.2595 nan 0.0100 0.0036
## 8 1.2515 nan 0.0100 0.0038
## 9 1.2436 nan 0.0100 0.0037
## 10 1.2348 nan 0.0100 0.0035
## 20 1.1624 nan 0.0100 0.0026
## 40 1.0462 nan 0.0100 0.0023
## 60 0.9555 nan 0.0100 0.0017
## 80 0.8847 nan 0.0100 0.0014
## 100 0.8285 nan 0.0100 0.0011
## 120 0.7841 nan 0.0100 0.0008
## 140 0.7476 nan 0.0100 0.0007
## 160 0.7164 nan 0.0100 0.0004
## 180 0.6886 nan 0.0100 0.0003
## 200 0.6638 nan 0.0100 0.0003
## 220 0.6414 nan 0.0100 0.0003
## 240 0.6212 nan 0.0100 0.0002
## 260 0.6050 nan 0.0100 0.0002
## 280 0.5893 nan 0.0100 0.0001
## 300 0.5746 nan 0.0100 0.0000
## 320 0.5615 nan 0.0100 0.0000
## 340 0.5485 nan 0.0100 0.0000
## 360 0.5356 nan 0.0100 -0.0001
## 380 0.5249 nan 0.0100 0.0001
## 400 0.5139 nan 0.0100 -0.0002
## 420 0.5031 nan 0.0100 -0.0000
## 440 0.4929 nan 0.0100 -0.0001
## 460 0.4836 nan 0.0100 -0.0000
## 480 0.4744 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0040
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2942 nan 0.0100 0.0044
## 4 1.2859 nan 0.0100 0.0036
## 5 1.2773 nan 0.0100 0.0035
## 6 1.2691 nan 0.0100 0.0036
## 7 1.2608 nan 0.0100 0.0041
## 8 1.2528 nan 0.0100 0.0036
## 9 1.2449 nan 0.0100 0.0038
## 10 1.2370 nan 0.0100 0.0031
## 20 1.1634 nan 0.0100 0.0032
## 40 1.0476 nan 0.0100 0.0021
## 60 0.9584 nan 0.0100 0.0015
## 80 0.8901 nan 0.0100 0.0013
## 100 0.8341 nan 0.0100 0.0012
## 120 0.7869 nan 0.0100 0.0009
## 140 0.7485 nan 0.0100 0.0004
## 160 0.7172 nan 0.0100 0.0004
## 180 0.6896 nan 0.0100 0.0002
## 200 0.6655 nan 0.0100 0.0002
## 220 0.6438 nan 0.0100 0.0001
## 240 0.6256 nan 0.0100 0.0003
## 260 0.6092 nan 0.0100 0.0000
## 280 0.5937 nan 0.0100 0.0000
## 300 0.5797 nan 0.0100 0.0000
## 320 0.5653 nan 0.0100 0.0001
## 340 0.5520 nan 0.0100 0.0000
## 360 0.5398 nan 0.0100 -0.0001
## 380 0.5291 nan 0.0100 0.0000
## 400 0.5179 nan 0.0100 -0.0000
## 420 0.5075 nan 0.0100 0.0000
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## 460 0.4893 nan 0.0100 0.0001
## 480 0.4798 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0040
## 2 1.3024 nan 0.0100 0.0039
## 3 1.2933 nan 0.0100 0.0040
## 4 1.2835 nan 0.0100 0.0046
## 5 1.2744 nan 0.0100 0.0043
## 6 1.2657 nan 0.0100 0.0040
## 7 1.2564 nan 0.0100 0.0043
## 8 1.2478 nan 0.0100 0.0040
## 9 1.2388 nan 0.0100 0.0040
## 10 1.2307 nan 0.0100 0.0033
## 20 1.1550 nan 0.0100 0.0028
## 40 1.0318 nan 0.0100 0.0024
## 60 0.9370 nan 0.0100 0.0018
## 80 0.8632 nan 0.0100 0.0014
## 100 0.8060 nan 0.0100 0.0006
## 120 0.7585 nan 0.0100 0.0006
## 140 0.7185 nan 0.0100 0.0005
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## 180 0.6549 nan 0.0100 0.0007
## 200 0.6284 nan 0.0100 0.0002
## 220 0.6048 nan 0.0100 0.0002
## 240 0.5850 nan 0.0100 0.0000
## 260 0.5662 nan 0.0100 0.0001
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## 300 0.5340 nan 0.0100 0.0001
## 320 0.5185 nan 0.0100 0.0001
## 340 0.5039 nan 0.0100 -0.0000
## 360 0.4912 nan 0.0100 0.0000
## 380 0.4786 nan 0.0100 -0.0001
## 400 0.4659 nan 0.0100 -0.0000
## 420 0.4546 nan 0.0100 -0.0001
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## 460 0.4334 nan 0.0100 0.0001
## 480 0.4244 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0038
## 2 1.3019 nan 0.0100 0.0047
## 3 1.2910 nan 0.0100 0.0048
## 4 1.2805 nan 0.0100 0.0047
## 5 1.2723 nan 0.0100 0.0040
## 6 1.2631 nan 0.0100 0.0043
## 7 1.2537 nan 0.0100 0.0042
## 8 1.2445 nan 0.0100 0.0039
## 9 1.2364 nan 0.0100 0.0040
## 10 1.2288 nan 0.0100 0.0031
## 20 1.1542 nan 0.0100 0.0031
## 40 1.0337 nan 0.0100 0.0027
## 60 0.9395 nan 0.0100 0.0017
## 80 0.8667 nan 0.0100 0.0014
## 100 0.8064 nan 0.0100 0.0013
## 120 0.7595 nan 0.0100 0.0006
## 140 0.7220 nan 0.0100 0.0005
## 160 0.6886 nan 0.0100 0.0003
## 180 0.6611 nan 0.0100 0.0001
## 200 0.6346 nan 0.0100 0.0004
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## 280 0.5545 nan 0.0100 0.0002
## 300 0.5386 nan 0.0100 0.0000
## 320 0.5245 nan 0.0100 0.0001
## 340 0.5109 nan 0.0100 -0.0001
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## 460 0.4419 nan 0.0100 -0.0001
## 480 0.4326 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3099 nan 0.0100 0.0047
## 2 1.3005 nan 0.0100 0.0045
## 3 1.2915 nan 0.0100 0.0043
## 4 1.2828 nan 0.0100 0.0037
## 5 1.2735 nan 0.0100 0.0044
## 6 1.2647 nan 0.0100 0.0041
## 7 1.2567 nan 0.0100 0.0032
## 8 1.2482 nan 0.0100 0.0038
## 9 1.2393 nan 0.0100 0.0041
## 10 1.2317 nan 0.0100 0.0035
## 20 1.1549 nan 0.0100 0.0037
## 40 1.0338 nan 0.0100 0.0027
## 60 0.9418 nan 0.0100 0.0018
## 80 0.8711 nan 0.0100 0.0012
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## 120 0.7669 nan 0.0100 0.0006
## 140 0.7268 nan 0.0100 0.0007
## 160 0.6945 nan 0.0100 0.0002
## 180 0.6654 nan 0.0100 0.0003
## 200 0.6415 nan 0.0100 -0.0001
## 220 0.6183 nan 0.0100 0.0002
## 240 0.5986 nan 0.0100 0.0000
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## 320 0.5327 nan 0.0100 0.0001
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## 360 0.5077 nan 0.0100 -0.0003
## 380 0.4950 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0362
## 2 1.1710 nan 0.1000 0.0260
## 3 1.1130 nan 0.1000 0.0255
## 4 1.0554 nan 0.1000 0.0243
## 5 1.0034 nan 0.1000 0.0214
## 6 0.9617 nan 0.1000 0.0186
## 7 0.9263 nan 0.1000 0.0130
## 8 0.8922 nan 0.1000 0.0150
## 9 0.8658 nan 0.1000 0.0113
## 10 0.8426 nan 0.1000 0.0087
## 20 0.6823 nan 0.1000 0.0042
## 40 0.5503 nan 0.1000 -0.0005
## 60 0.4665 nan 0.1000 0.0003
## 80 0.4030 nan 0.1000 -0.0005
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## 120 0.3096 nan 0.1000 -0.0001
## 140 0.2773 nan 0.1000 -0.0003
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## 180 0.2282 nan 0.1000 -0.0002
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## 460 0.0617 nan 0.1000 -0.0001
## 480 0.0563 nan 0.1000 -0.0001
## 500 0.0521 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.1000 0.0415
## 2 1.1683 nan 0.1000 0.0301
## 3 1.1056 nan 0.1000 0.0283
## 4 1.0535 nan 0.1000 0.0223
## 5 1.0095 nan 0.1000 0.0197
## 6 0.9746 nan 0.1000 0.0136
## 7 0.9401 nan 0.1000 0.0132
## 8 0.9106 nan 0.1000 0.0123
## 9 0.8821 nan 0.1000 0.0104
## 10 0.8532 nan 0.1000 0.0111
## 20 0.6928 nan 0.1000 0.0019
## 40 0.5538 nan 0.1000 -0.0002
## 60 0.4785 nan 0.1000 -0.0003
## 80 0.4160 nan 0.1000 -0.0000
## 100 0.3627 nan 0.1000 -0.0002
## 120 0.3154 nan 0.1000 -0.0001
## 140 0.2827 nan 0.1000 -0.0006
## 160 0.2513 nan 0.1000 -0.0002
## 180 0.2274 nan 0.1000 -0.0004
## 200 0.2078 nan 0.1000 0.0000
## 220 0.1901 nan 0.1000 -0.0002
## 240 0.1726 nan 0.1000 -0.0007
## 260 0.1559 nan 0.1000 -0.0004
## 280 0.1433 nan 0.1000 -0.0006
## 300 0.1309 nan 0.1000 -0.0006
## 320 0.1189 nan 0.1000 -0.0001
## 340 0.1096 nan 0.1000 -0.0002
## 360 0.1010 nan 0.1000 -0.0005
## 380 0.0922 nan 0.1000 -0.0003
## 400 0.0844 nan 0.1000 -0.0003
## 420 0.0779 nan 0.1000 -0.0002
## 440 0.0714 nan 0.1000 -0.0003
## 460 0.0661 nan 0.1000 -0.0001
## 480 0.0609 nan 0.1000 0.0001
## 500 0.0563 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2340 nan 0.1000 0.0385
## 2 1.1709 nan 0.1000 0.0248
## 3 1.1110 nan 0.1000 0.0240
## 4 1.0545 nan 0.1000 0.0271
## 5 1.0102 nan 0.1000 0.0194
## 6 0.9686 nan 0.1000 0.0198
## 7 0.9356 nan 0.1000 0.0119
## 8 0.9073 nan 0.1000 0.0133
## 9 0.8794 nan 0.1000 0.0123
## 10 0.8561 nan 0.1000 0.0103
## 20 0.6948 nan 0.1000 0.0028
## 40 0.5612 nan 0.1000 -0.0014
## 60 0.4826 nan 0.1000 0.0001
## 80 0.4252 nan 0.1000 0.0009
## 100 0.3733 nan 0.1000 0.0004
## 120 0.3315 nan 0.1000 -0.0020
## 140 0.3007 nan 0.1000 -0.0008
## 160 0.2701 nan 0.1000 -0.0010
## 180 0.2413 nan 0.1000 -0.0006
## 200 0.2201 nan 0.1000 -0.0011
## 220 0.1977 nan 0.1000 -0.0010
## 240 0.1781 nan 0.1000 -0.0002
## 260 0.1608 nan 0.1000 -0.0008
## 280 0.1489 nan 0.1000 -0.0003
## 300 0.1367 nan 0.1000 -0.0006
## 320 0.1239 nan 0.1000 -0.0007
## 340 0.1144 nan 0.1000 -0.0003
## 360 0.1074 nan 0.1000 -0.0004
## 380 0.0989 nan 0.1000 -0.0004
## 400 0.0919 nan 0.1000 -0.0005
## 420 0.0840 nan 0.1000 -0.0001
## 440 0.0776 nan 0.1000 -0.0003
## 460 0.0715 nan 0.1000 -0.0002
## 480 0.0658 nan 0.1000 -0.0002
## 500 0.0606 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2294 nan 0.1000 0.0416
## 2 1.1550 nan 0.1000 0.0317
## 3 1.0948 nan 0.1000 0.0243
## 4 1.0397 nan 0.1000 0.0238
## 5 0.9916 nan 0.1000 0.0188
## 6 0.9509 nan 0.1000 0.0197
## 7 0.9172 nan 0.1000 0.0111
## 8 0.8822 nan 0.1000 0.0146
## 9 0.8548 nan 0.1000 0.0095
## 10 0.8260 nan 0.1000 0.0107
## 20 0.6525 nan 0.1000 0.0026
## 40 0.5086 nan 0.1000 -0.0007
## 60 0.4250 nan 0.1000 -0.0004
## 80 0.3579 nan 0.1000 -0.0007
## 100 0.3044 nan 0.1000 0.0001
## 120 0.2673 nan 0.1000 -0.0001
## 140 0.2299 nan 0.1000 -0.0007
## 160 0.1999 nan 0.1000 -0.0004
## 180 0.1775 nan 0.1000 -0.0007
## 200 0.1541 nan 0.1000 -0.0004
## 220 0.1358 nan 0.1000 -0.0001
## 240 0.1197 nan 0.1000 -0.0005
## 260 0.1076 nan 0.1000 0.0000
## 280 0.0963 nan 0.1000 -0.0001
## 300 0.0860 nan 0.1000 -0.0001
## 320 0.0765 nan 0.1000 -0.0003
## 340 0.0697 nan 0.1000 -0.0002
## 360 0.0629 nan 0.1000 -0.0001
## 380 0.0569 nan 0.1000 -0.0001
## 400 0.0514 nan 0.1000 -0.0001
## 420 0.0463 nan 0.1000 -0.0001
## 440 0.0417 nan 0.1000 -0.0003
## 460 0.0377 nan 0.1000 -0.0001
## 480 0.0340 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2319 nan 0.1000 0.0388
## 2 1.1571 nan 0.1000 0.0308
## 3 1.0934 nan 0.1000 0.0265
## 4 1.0392 nan 0.1000 0.0233
## 5 0.9942 nan 0.1000 0.0203
## 6 0.9508 nan 0.1000 0.0175
## 7 0.9136 nan 0.1000 0.0163
## 8 0.8850 nan 0.1000 0.0122
## 9 0.8568 nan 0.1000 0.0109
## 10 0.8311 nan 0.1000 0.0070
## 20 0.6659 nan 0.1000 0.0046
## 40 0.5144 nan 0.1000 0.0007
## 60 0.4251 nan 0.1000 0.0000
## 80 0.3657 nan 0.1000 -0.0014
## 100 0.3116 nan 0.1000 -0.0014
## 120 0.2711 nan 0.1000 -0.0004
## 140 0.2349 nan 0.1000 -0.0008
## 160 0.2067 nan 0.1000 -0.0003
## 180 0.1812 nan 0.1000 -0.0007
## 200 0.1579 nan 0.1000 -0.0004
## 220 0.1392 nan 0.1000 -0.0004
## 240 0.1228 nan 0.1000 -0.0005
## 260 0.1092 nan 0.1000 -0.0005
## 280 0.0964 nan 0.1000 -0.0002
## 300 0.0872 nan 0.1000 -0.0002
## 320 0.0787 nan 0.1000 -0.0002
## 340 0.0696 nan 0.1000 0.0000
## 360 0.0626 nan 0.1000 -0.0002
## 380 0.0567 nan 0.1000 -0.0003
## 400 0.0507 nan 0.1000 -0.0001
## 420 0.0460 nan 0.1000 -0.0001
## 440 0.0416 nan 0.1000 -0.0001
## 460 0.0378 nan 0.1000 -0.0002
## 480 0.0340 nan 0.1000 -0.0001
## 500 0.0305 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2418 nan 0.1000 0.0333
## 2 1.1624 nan 0.1000 0.0365
## 3 1.0999 nan 0.1000 0.0283
## 4 1.0443 nan 0.1000 0.0210
## 5 0.9975 nan 0.1000 0.0207
## 6 0.9558 nan 0.1000 0.0175
## 7 0.9186 nan 0.1000 0.0146
## 8 0.8840 nan 0.1000 0.0124
## 9 0.8533 nan 0.1000 0.0111
## 10 0.8218 nan 0.1000 0.0128
## 20 0.6663 nan 0.1000 0.0028
## 40 0.5271 nan 0.1000 -0.0000
## 60 0.4425 nan 0.1000 -0.0004
## 80 0.3795 nan 0.1000 -0.0011
## 100 0.3254 nan 0.1000 -0.0006
## 120 0.2800 nan 0.1000 -0.0010
## 140 0.2450 nan 0.1000 -0.0007
## 160 0.2099 nan 0.1000 -0.0006
## 180 0.1836 nan 0.1000 -0.0001
## 200 0.1603 nan 0.1000 -0.0008
## 220 0.1423 nan 0.1000 -0.0003
## 240 0.1259 nan 0.1000 -0.0005
## 260 0.1130 nan 0.1000 -0.0006
## 280 0.1017 nan 0.1000 -0.0003
## 300 0.0913 nan 0.1000 -0.0002
## 320 0.0823 nan 0.1000 -0.0008
## 340 0.0742 nan 0.1000 -0.0000
## 360 0.0659 nan 0.1000 -0.0002
## 380 0.0594 nan 0.1000 -0.0001
## 400 0.0538 nan 0.1000 -0.0002
## 420 0.0484 nan 0.1000 -0.0002
## 440 0.0439 nan 0.1000 -0.0001
## 460 0.0397 nan 0.1000 -0.0002
## 480 0.0361 nan 0.1000 -0.0001
## 500 0.0326 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2334 nan 0.1000 0.0370
## 2 1.1564 nan 0.1000 0.0347
## 3 1.0940 nan 0.1000 0.0257
## 4 1.0399 nan 0.1000 0.0230
## 5 0.9841 nan 0.1000 0.0225
## 6 0.9410 nan 0.1000 0.0173
## 7 0.9031 nan 0.1000 0.0162
## 8 0.8645 nan 0.1000 0.0164
## 9 0.8330 nan 0.1000 0.0141
## 10 0.8044 nan 0.1000 0.0103
## 20 0.6343 nan 0.1000 0.0035
## 40 0.4703 nan 0.1000 -0.0001
## 60 0.3712 nan 0.1000 -0.0002
## 80 0.3094 nan 0.1000 -0.0010
## 100 0.2570 nan 0.1000 -0.0003
## 120 0.2136 nan 0.1000 -0.0006
## 140 0.1824 nan 0.1000 -0.0004
## 160 0.1549 nan 0.1000 -0.0005
## 180 0.1319 nan 0.1000 0.0002
## 200 0.1128 nan 0.1000 -0.0003
## 220 0.0987 nan 0.1000 -0.0003
## 240 0.0855 nan 0.1000 -0.0002
## 260 0.0743 nan 0.1000 -0.0001
## 280 0.0650 nan 0.1000 -0.0003
## 300 0.0563 nan 0.1000 -0.0001
## 320 0.0494 nan 0.1000 -0.0000
## 340 0.0439 nan 0.1000 -0.0001
## 360 0.0392 nan 0.1000 -0.0001
## 380 0.0345 nan 0.1000 -0.0000
## 400 0.0304 nan 0.1000 -0.0001
## 420 0.0269 nan 0.1000 -0.0001
## 440 0.0237 nan 0.1000 -0.0001
## 460 0.0209 nan 0.1000 -0.0001
## 480 0.0184 nan 0.1000 -0.0001
## 500 0.0164 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2251 nan 0.1000 0.0421
## 2 1.1527 nan 0.1000 0.0330
## 3 1.0842 nan 0.1000 0.0295
## 4 1.0286 nan 0.1000 0.0230
## 5 0.9785 nan 0.1000 0.0200
## 6 0.9341 nan 0.1000 0.0174
## 7 0.8966 nan 0.1000 0.0144
## 8 0.8612 nan 0.1000 0.0119
## 9 0.8346 nan 0.1000 0.0089
## 10 0.8122 nan 0.1000 0.0099
## 20 0.6520 nan 0.1000 0.0021
## 40 0.4859 nan 0.1000 -0.0008
## 60 0.3838 nan 0.1000 -0.0001
## 80 0.3218 nan 0.1000 -0.0014
## 100 0.2645 nan 0.1000 0.0004
## 120 0.2221 nan 0.1000 -0.0006
## 140 0.1859 nan 0.1000 -0.0016
## 160 0.1596 nan 0.1000 -0.0006
## 180 0.1362 nan 0.1000 -0.0003
## 200 0.1172 nan 0.1000 -0.0005
## 220 0.1018 nan 0.1000 -0.0003
## 240 0.0885 nan 0.1000 -0.0003
## 260 0.0766 nan 0.1000 -0.0004
## 280 0.0671 nan 0.1000 -0.0001
## 300 0.0587 nan 0.1000 -0.0001
## 320 0.0517 nan 0.1000 -0.0002
## 340 0.0461 nan 0.1000 -0.0002
## 360 0.0401 nan 0.1000 -0.0000
## 380 0.0358 nan 0.1000 -0.0001
## 400 0.0322 nan 0.1000 -0.0000
## 420 0.0284 nan 0.1000 -0.0002
## 440 0.0250 nan 0.1000 -0.0000
## 460 0.0219 nan 0.1000 -0.0001
## 480 0.0194 nan 0.1000 -0.0002
## 500 0.0173 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2203 nan 0.1000 0.0452
## 2 1.1399 nan 0.1000 0.0359
## 3 1.0795 nan 0.1000 0.0277
## 4 1.0241 nan 0.1000 0.0238
## 5 0.9755 nan 0.1000 0.0216
## 6 0.9349 nan 0.1000 0.0168
## 7 0.9006 nan 0.1000 0.0127
## 8 0.8702 nan 0.1000 0.0119
## 9 0.8371 nan 0.1000 0.0120
## 10 0.8123 nan 0.1000 0.0084
## 20 0.6361 nan 0.1000 0.0026
## 40 0.4837 nan 0.1000 0.0016
## 60 0.3914 nan 0.1000 -0.0006
## 80 0.3241 nan 0.1000 -0.0000
## 100 0.2706 nan 0.1000 -0.0007
## 120 0.2256 nan 0.1000 -0.0001
## 140 0.1925 nan 0.1000 -0.0006
## 160 0.1648 nan 0.1000 -0.0006
## 180 0.1423 nan 0.1000 -0.0005
## 200 0.1226 nan 0.1000 -0.0006
## 220 0.1053 nan 0.1000 -0.0007
## 240 0.0920 nan 0.1000 -0.0005
## 260 0.0806 nan 0.1000 -0.0002
## 280 0.0703 nan 0.1000 -0.0003
## 300 0.0618 nan 0.1000 -0.0001
## 320 0.0549 nan 0.1000 -0.0004
## 340 0.0490 nan 0.1000 0.0001
## 360 0.0430 nan 0.1000 -0.0003
## 380 0.0384 nan 0.1000 -0.0002
## 400 0.0336 nan 0.1000 -0.0001
## 420 0.0299 nan 0.1000 -0.0001
## 440 0.0265 nan 0.1000 -0.0001
## 460 0.0235 nan 0.1000 -0.0001
## 480 0.0210 nan 0.1000 -0.0001
## 500 0.0186 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3172 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0003
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3041 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2584 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2303 nan 0.0010 0.0003
## 140 1.2168 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1912 nan 0.0010 0.0003
## 200 1.1790 nan 0.0010 0.0002
## 220 1.1672 nan 0.0010 0.0003
## 240 1.1558 nan 0.0010 0.0003
## 260 1.1446 nan 0.0010 0.0002
## 280 1.1338 nan 0.0010 0.0002
## 300 1.1232 nan 0.0010 0.0002
## 320 1.1131 nan 0.0010 0.0002
## 340 1.1033 nan 0.0010 0.0002
## 360 1.0936 nan 0.0010 0.0002
## 380 1.0841 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0661 nan 0.0010 0.0002
## 440 1.0575 nan 0.0010 0.0002
## 460 1.0491 nan 0.0010 0.0001
## 480 1.0410 nan 0.0010 0.0001
## 500 1.0332 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0003
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3043 nan 0.0010 0.0004
## 40 1.2882 nan 0.0010 0.0004
## 60 1.2729 nan 0.0010 0.0003
## 80 1.2583 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2309 nan 0.0010 0.0002
## 140 1.2174 nan 0.0010 0.0003
## 160 1.2044 nan 0.0010 0.0003
## 180 1.1916 nan 0.0010 0.0003
## 200 1.1793 nan 0.0010 0.0003
## 220 1.1679 nan 0.0010 0.0002
## 240 1.1567 nan 0.0010 0.0002
## 260 1.1455 nan 0.0010 0.0002
## 280 1.1349 nan 0.0010 0.0002
## 300 1.1246 nan 0.0010 0.0002
## 320 1.1147 nan 0.0010 0.0002
## 340 1.1047 nan 0.0010 0.0002
## 360 1.0951 nan 0.0010 0.0002
## 380 1.0853 nan 0.0010 0.0002
## 400 1.0764 nan 0.0010 0.0002
## 420 1.0677 nan 0.0010 0.0002
## 440 1.0590 nan 0.0010 0.0002
## 460 1.0505 nan 0.0010 0.0002
## 480 1.0422 nan 0.0010 0.0002
## 500 1.0342 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0003
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0003
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3126 nan 0.0010 0.0003
## 20 1.3044 nan 0.0010 0.0004
## 40 1.2886 nan 0.0010 0.0004
## 60 1.2736 nan 0.0010 0.0004
## 80 1.2590 nan 0.0010 0.0004
## 100 1.2454 nan 0.0010 0.0003
## 120 1.2316 nan 0.0010 0.0003
## 140 1.2185 nan 0.0010 0.0003
## 160 1.2059 nan 0.0010 0.0003
## 180 1.1934 nan 0.0010 0.0003
## 200 1.1814 nan 0.0010 0.0003
## 220 1.1696 nan 0.0010 0.0003
## 240 1.1583 nan 0.0010 0.0002
## 260 1.1473 nan 0.0010 0.0002
## 280 1.1367 nan 0.0010 0.0002
## 300 1.1263 nan 0.0010 0.0002
## 320 1.1161 nan 0.0010 0.0002
## 340 1.1062 nan 0.0010 0.0002
## 360 1.0967 nan 0.0010 0.0002
## 380 1.0872 nan 0.0010 0.0002
## 400 1.0782 nan 0.0010 0.0002
## 420 1.0695 nan 0.0010 0.0001
## 440 1.0608 nan 0.0010 0.0002
## 460 1.0523 nan 0.0010 0.0002
## 480 1.0441 nan 0.0010 0.0001
## 500 1.0362 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0003
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3146 nan 0.0010 0.0003
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3032 nan 0.0010 0.0004
## 40 1.2866 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0004
## 80 1.2547 nan 0.0010 0.0004
## 100 1.2395 nan 0.0010 0.0004
## 120 1.2252 nan 0.0010 0.0003
## 140 1.2109 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1838 nan 0.0010 0.0002
## 200 1.1709 nan 0.0010 0.0003
## 220 1.1586 nan 0.0010 0.0003
## 240 1.1468 nan 0.0010 0.0003
## 260 1.1351 nan 0.0010 0.0002
## 280 1.1236 nan 0.0010 0.0003
## 300 1.1124 nan 0.0010 0.0002
## 320 1.1015 nan 0.0010 0.0003
## 340 1.0907 nan 0.0010 0.0002
## 360 1.0804 nan 0.0010 0.0002
## 380 1.0703 nan 0.0010 0.0002
## 400 1.0606 nan 0.0010 0.0002
## 420 1.0513 nan 0.0010 0.0002
## 440 1.0422 nan 0.0010 0.0002
## 460 1.0333 nan 0.0010 0.0002
## 480 1.0246 nan 0.0010 0.0002
## 500 1.0162 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0004
## 60 1.2696 nan 0.0010 0.0004
## 80 1.2539 nan 0.0010 0.0004
## 100 1.2392 nan 0.0010 0.0003
## 120 1.2245 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1965 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1703 nan 0.0010 0.0003
## 220 1.1579 nan 0.0010 0.0002
## 240 1.1458 nan 0.0010 0.0002
## 260 1.1340 nan 0.0010 0.0003
## 280 1.1225 nan 0.0010 0.0003
## 300 1.1117 nan 0.0010 0.0002
## 320 1.1012 nan 0.0010 0.0002
## 340 1.0906 nan 0.0010 0.0003
## 360 1.0803 nan 0.0010 0.0002
## 380 1.0707 nan 0.0010 0.0002
## 400 1.0610 nan 0.0010 0.0002
## 420 1.0516 nan 0.0010 0.0002
## 440 1.0428 nan 0.0010 0.0002
## 460 1.0342 nan 0.0010 0.0002
## 480 1.0256 nan 0.0010 0.0002
## 500 1.0173 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3128 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2867 nan 0.0010 0.0004
## 60 1.2704 nan 0.0010 0.0004
## 80 1.2551 nan 0.0010 0.0003
## 100 1.2401 nan 0.0010 0.0003
## 120 1.2254 nan 0.0010 0.0003
## 140 1.2116 nan 0.0010 0.0002
## 160 1.1980 nan 0.0010 0.0003
## 180 1.1848 nan 0.0010 0.0003
## 200 1.1721 nan 0.0010 0.0003
## 220 1.1598 nan 0.0010 0.0003
## 240 1.1478 nan 0.0010 0.0003
## 260 1.1359 nan 0.0010 0.0003
## 280 1.1250 nan 0.0010 0.0003
## 300 1.1140 nan 0.0010 0.0002
## 320 1.1038 nan 0.0010 0.0002
## 340 1.0935 nan 0.0010 0.0002
## 360 1.0832 nan 0.0010 0.0002
## 380 1.0733 nan 0.0010 0.0002
## 400 1.0637 nan 0.0010 0.0002
## 420 1.0544 nan 0.0010 0.0002
## 440 1.0454 nan 0.0010 0.0002
## 460 1.0365 nan 0.0010 0.0002
## 480 1.0278 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3141 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3122 nan 0.0010 0.0004
## 10 1.3114 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2846 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0004
## 100 1.2352 nan 0.0010 0.0004
## 120 1.2199 nan 0.0010 0.0003
## 140 1.2051 nan 0.0010 0.0004
## 160 1.1906 nan 0.0010 0.0003
## 180 1.1768 nan 0.0010 0.0003
## 200 1.1631 nan 0.0010 0.0003
## 220 1.1499 nan 0.0010 0.0003
## 240 1.1373 nan 0.0010 0.0003
## 260 1.1249 nan 0.0010 0.0003
## 280 1.1129 nan 0.0010 0.0003
## 300 1.1015 nan 0.0010 0.0002
## 320 1.0903 nan 0.0010 0.0002
## 340 1.0794 nan 0.0010 0.0002
## 360 1.0689 nan 0.0010 0.0002
## 380 1.0586 nan 0.0010 0.0002
## 400 1.0484 nan 0.0010 0.0002
## 420 1.0384 nan 0.0010 0.0002
## 440 1.0290 nan 0.0010 0.0002
## 460 1.0196 nan 0.0010 0.0002
## 480 1.0108 nan 0.0010 0.0002
## 500 1.0021 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3179 nan 0.0010 0.0004
## 4 1.3170 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2684 nan 0.0010 0.0003
## 80 1.2522 nan 0.0010 0.0004
## 100 1.2363 nan 0.0010 0.0004
## 120 1.2208 nan 0.0010 0.0004
## 140 1.2059 nan 0.0010 0.0003
## 160 1.1915 nan 0.0010 0.0003
## 180 1.1775 nan 0.0010 0.0003
## 200 1.1641 nan 0.0010 0.0003
## 220 1.1511 nan 0.0010 0.0003
## 240 1.1387 nan 0.0010 0.0002
## 260 1.1265 nan 0.0010 0.0003
## 280 1.1145 nan 0.0010 0.0003
## 300 1.1031 nan 0.0010 0.0002
## 320 1.0919 nan 0.0010 0.0002
## 340 1.0809 nan 0.0010 0.0002
## 360 1.0703 nan 0.0010 0.0002
## 380 1.0600 nan 0.0010 0.0002
## 400 1.0501 nan 0.0010 0.0002
## 420 1.0403 nan 0.0010 0.0002
## 440 1.0307 nan 0.0010 0.0002
## 460 1.0216 nan 0.0010 0.0002
## 480 1.0126 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3152 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2685 nan 0.0010 0.0004
## 80 1.2523 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0003
## 120 1.2217 nan 0.0010 0.0003
## 140 1.2073 nan 0.0010 0.0003
## 160 1.1928 nan 0.0010 0.0003
## 180 1.1790 nan 0.0010 0.0003
## 200 1.1657 nan 0.0010 0.0002
## 220 1.1528 nan 0.0010 0.0003
## 240 1.1405 nan 0.0010 0.0002
## 260 1.1283 nan 0.0010 0.0003
## 280 1.1165 nan 0.0010 0.0003
## 300 1.1051 nan 0.0010 0.0002
## 320 1.0942 nan 0.0010 0.0003
## 340 1.0836 nan 0.0010 0.0002
## 360 1.0730 nan 0.0010 0.0002
## 380 1.0629 nan 0.0010 0.0002
## 400 1.0529 nan 0.0010 0.0002
## 420 1.0432 nan 0.0010 0.0002
## 440 1.0338 nan 0.0010 0.0002
## 460 1.0246 nan 0.0010 0.0002
## 480 1.0159 nan 0.0010 0.0002
## 500 1.0070 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3127 nan 0.0100 0.0039
## 2 1.3044 nan 0.0100 0.0036
## 3 1.2966 nan 0.0100 0.0034
## 4 1.2881 nan 0.0100 0.0037
## 5 1.2799 nan 0.0100 0.0039
## 6 1.2729 nan 0.0100 0.0033
## 7 1.2658 nan 0.0100 0.0031
## 8 1.2583 nan 0.0100 0.0034
## 9 1.2509 nan 0.0100 0.0033
## 10 1.2438 nan 0.0100 0.0033
## 20 1.1806 nan 0.0100 0.0026
## 40 1.0759 nan 0.0100 0.0019
## 60 0.9953 nan 0.0100 0.0013
## 80 0.9336 nan 0.0100 0.0008
## 100 0.8835 nan 0.0100 0.0009
## 120 0.8425 nan 0.0100 0.0007
## 140 0.8094 nan 0.0100 0.0006
## 160 0.7804 nan 0.0100 0.0005
## 180 0.7568 nan 0.0100 0.0001
## 200 0.7348 nan 0.0100 0.0002
## 220 0.7169 nan 0.0100 -0.0001
## 240 0.6996 nan 0.0100 -0.0000
## 260 0.6828 nan 0.0100 0.0000
## 280 0.6679 nan 0.0100 0.0001
## 300 0.6532 nan 0.0100 0.0002
## 320 0.6408 nan 0.0100 -0.0000
## 340 0.6284 nan 0.0100 -0.0000
## 360 0.6172 nan 0.0100 0.0001
## 380 0.6061 nan 0.0100 0.0000
## 400 0.5961 nan 0.0100 0.0000
## 420 0.5872 nan 0.0100 -0.0001
## 440 0.5786 nan 0.0100 -0.0001
## 460 0.5701 nan 0.0100 0.0001
## 480 0.5606 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0037
## 2 1.3041 nan 0.0100 0.0039
## 3 1.2955 nan 0.0100 0.0035
## 4 1.2879 nan 0.0100 0.0038
## 5 1.2796 nan 0.0100 0.0038
## 6 1.2723 nan 0.0100 0.0034
## 7 1.2653 nan 0.0100 0.0030
## 8 1.2581 nan 0.0100 0.0032
## 9 1.2503 nan 0.0100 0.0034
## 10 1.2428 nan 0.0100 0.0032
## 20 1.1784 nan 0.0100 0.0024
## 40 1.0732 nan 0.0100 0.0018
## 60 0.9941 nan 0.0100 0.0014
## 80 0.9330 nan 0.0100 0.0008
## 100 0.8827 nan 0.0100 0.0008
## 120 0.8422 nan 0.0100 0.0006
## 140 0.8079 nan 0.0100 0.0005
## 160 0.7794 nan 0.0100 0.0003
## 180 0.7559 nan 0.0100 0.0002
## 200 0.7348 nan 0.0100 0.0000
## 220 0.7166 nan 0.0100 -0.0000
## 240 0.6997 nan 0.0100 0.0001
## 260 0.6846 nan 0.0100 0.0000
## 280 0.6709 nan 0.0100 0.0000
## 300 0.6576 nan 0.0100 -0.0000
## 320 0.6462 nan 0.0100 -0.0000
## 340 0.6356 nan 0.0100 0.0000
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## 380 0.6155 nan 0.0100 0.0001
## 400 0.6069 nan 0.0100 -0.0000
## 420 0.5976 nan 0.0100 -0.0000
## 440 0.5881 nan 0.0100 -0.0002
## 460 0.5787 nan 0.0100 -0.0001
## 480 0.5701 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0036
## 2 1.3038 nan 0.0100 0.0034
## 3 1.2964 nan 0.0100 0.0033
## 4 1.2888 nan 0.0100 0.0038
## 5 1.2811 nan 0.0100 0.0033
## 6 1.2732 nan 0.0100 0.0036
## 7 1.2663 nan 0.0100 0.0032
## 8 1.2590 nan 0.0100 0.0035
## 9 1.2514 nan 0.0100 0.0036
## 10 1.2443 nan 0.0100 0.0032
## 20 1.1790 nan 0.0100 0.0024
## 40 1.0771 nan 0.0100 0.0020
## 60 0.9993 nan 0.0100 0.0012
## 80 0.9368 nan 0.0100 0.0009
## 100 0.8878 nan 0.0100 0.0007
## 120 0.8470 nan 0.0100 0.0007
## 140 0.8130 nan 0.0100 0.0006
## 160 0.7853 nan 0.0100 0.0002
## 180 0.7612 nan 0.0100 0.0003
## 200 0.7401 nan 0.0100 0.0000
## 220 0.7220 nan 0.0100 -0.0001
## 240 0.7051 nan 0.0100 -0.0000
## 260 0.6901 nan 0.0100 0.0001
## 280 0.6767 nan 0.0100 0.0002
## 300 0.6645 nan 0.0100 0.0001
## 320 0.6540 nan 0.0100 -0.0001
## 340 0.6430 nan 0.0100 -0.0002
## 360 0.6320 nan 0.0100 -0.0001
## 380 0.6227 nan 0.0100 0.0000
## 400 0.6139 nan 0.0100 -0.0000
## 420 0.6046 nan 0.0100 -0.0001
## 440 0.5965 nan 0.0100 -0.0002
## 460 0.5881 nan 0.0100 -0.0000
## 480 0.5800 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3026 nan 0.0100 0.0041
## 3 1.2937 nan 0.0100 0.0040
## 4 1.2852 nan 0.0100 0.0041
## 5 1.2770 nan 0.0100 0.0037
## 6 1.2695 nan 0.0100 0.0034
## 7 1.2612 nan 0.0100 0.0037
## 8 1.2533 nan 0.0100 0.0034
## 9 1.2450 nan 0.0100 0.0036
## 10 1.2372 nan 0.0100 0.0032
## 20 1.1681 nan 0.0100 0.0029
## 40 1.0579 nan 0.0100 0.0021
## 60 0.9747 nan 0.0100 0.0014
## 80 0.9091 nan 0.0100 0.0010
## 100 0.8572 nan 0.0100 0.0006
## 120 0.8145 nan 0.0100 0.0005
## 140 0.7789 nan 0.0100 0.0003
## 160 0.7486 nan 0.0100 0.0003
## 180 0.7211 nan 0.0100 0.0004
## 200 0.6982 nan 0.0100 0.0003
## 220 0.6780 nan 0.0100 0.0003
## 240 0.6594 nan 0.0100 -0.0000
## 260 0.6423 nan 0.0100 0.0001
## 280 0.6272 nan 0.0100 0.0001
## 300 0.6137 nan 0.0100 0.0001
## 320 0.6000 nan 0.0100 0.0000
## 340 0.5879 nan 0.0100 -0.0001
## 360 0.5752 nan 0.0100 0.0001
## 380 0.5632 nan 0.0100 0.0000
## 400 0.5521 nan 0.0100 -0.0000
## 420 0.5418 nan 0.0100 -0.0002
## 440 0.5318 nan 0.0100 -0.0001
## 460 0.5221 nan 0.0100 -0.0001
## 480 0.5123 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3122 nan 0.0100 0.0033
## 2 1.3035 nan 0.0100 0.0038
## 3 1.2957 nan 0.0100 0.0036
## 4 1.2875 nan 0.0100 0.0036
## 5 1.2788 nan 0.0100 0.0034
## 6 1.2710 nan 0.0100 0.0038
## 7 1.2631 nan 0.0100 0.0035
## 8 1.2546 nan 0.0100 0.0038
## 9 1.2469 nan 0.0100 0.0035
## 10 1.2394 nan 0.0100 0.0035
## 20 1.1710 nan 0.0100 0.0025
## 40 1.0597 nan 0.0100 0.0019
## 60 0.9785 nan 0.0100 0.0014
## 80 0.9136 nan 0.0100 0.0009
## 100 0.8612 nan 0.0100 0.0010
## 120 0.8190 nan 0.0100 0.0008
## 140 0.7841 nan 0.0100 0.0003
## 160 0.7521 nan 0.0100 0.0003
## 180 0.7257 nan 0.0100 0.0004
## 200 0.7041 nan 0.0100 0.0003
## 220 0.6831 nan 0.0100 0.0000
## 240 0.6662 nan 0.0100 0.0002
## 260 0.6499 nan 0.0100 0.0002
## 280 0.6352 nan 0.0100 0.0001
## 300 0.6216 nan 0.0100 -0.0000
## 320 0.6084 nan 0.0100 0.0002
## 340 0.5960 nan 0.0100 0.0000
## 360 0.5846 nan 0.0100 0.0000
## 380 0.5733 nan 0.0100 0.0000
## 400 0.5614 nan 0.0100 -0.0001
## 420 0.5509 nan 0.0100 -0.0001
## 440 0.5404 nan 0.0100 -0.0000
## 460 0.5305 nan 0.0100 -0.0000
## 480 0.5211 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0041
## 3 1.2952 nan 0.0100 0.0040
## 4 1.2869 nan 0.0100 0.0035
## 5 1.2790 nan 0.0100 0.0033
## 6 1.2710 nan 0.0100 0.0035
## 7 1.2628 nan 0.0100 0.0035
## 8 1.2551 nan 0.0100 0.0031
## 9 1.2478 nan 0.0100 0.0033
## 10 1.2406 nan 0.0100 0.0035
## 20 1.1728 nan 0.0100 0.0025
## 40 1.0644 nan 0.0100 0.0021
## 60 0.9824 nan 0.0100 0.0015
## 80 0.9185 nan 0.0100 0.0010
## 100 0.8682 nan 0.0100 0.0007
## 120 0.8245 nan 0.0100 0.0008
## 140 0.7895 nan 0.0100 0.0004
## 160 0.7593 nan 0.0100 0.0005
## 180 0.7336 nan 0.0100 0.0002
## 200 0.7122 nan 0.0100 0.0002
## 220 0.6917 nan 0.0100 0.0003
## 240 0.6735 nan 0.0100 0.0002
## 260 0.6570 nan 0.0100 0.0001
## 280 0.6430 nan 0.0100 -0.0002
## 300 0.6292 nan 0.0100 0.0002
## 320 0.6169 nan 0.0100 -0.0002
## 340 0.6048 nan 0.0100 0.0001
## 360 0.5925 nan 0.0100 -0.0001
## 380 0.5815 nan 0.0100 0.0000
## 400 0.5695 nan 0.0100 -0.0001
## 420 0.5594 nan 0.0100 0.0000
## 440 0.5499 nan 0.0100 -0.0001
## 460 0.5410 nan 0.0100 -0.0002
## 480 0.5321 nan 0.0100 -0.0001
## 500 0.5224 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0044
## 2 1.3019 nan 0.0100 0.0041
## 3 1.2931 nan 0.0100 0.0041
## 4 1.2844 nan 0.0100 0.0042
## 5 1.2761 nan 0.0100 0.0039
## 6 1.2680 nan 0.0100 0.0035
## 7 1.2599 nan 0.0100 0.0038
## 8 1.2519 nan 0.0100 0.0035
## 9 1.2439 nan 0.0100 0.0039
## 10 1.2358 nan 0.0100 0.0036
## 20 1.1632 nan 0.0100 0.0030
## 40 1.0480 nan 0.0100 0.0021
## 60 0.9606 nan 0.0100 0.0016
## 80 0.8928 nan 0.0100 0.0013
## 100 0.8379 nan 0.0100 0.0006
## 120 0.7924 nan 0.0100 0.0004
## 140 0.7549 nan 0.0100 0.0003
## 160 0.7229 nan 0.0100 0.0002
## 180 0.6939 nan 0.0100 0.0004
## 200 0.6695 nan 0.0100 0.0001
## 220 0.6468 nan 0.0100 -0.0000
## 240 0.6271 nan 0.0100 0.0000
## 260 0.6099 nan 0.0100 0.0001
## 280 0.5935 nan 0.0100 0.0001
## 300 0.5777 nan 0.0100 0.0000
## 320 0.5639 nan 0.0100 0.0000
## 340 0.5503 nan 0.0100 0.0002
## 360 0.5370 nan 0.0100 0.0001
## 380 0.5239 nan 0.0100 0.0000
## 400 0.5120 nan 0.0100 -0.0000
## 420 0.5003 nan 0.0100 -0.0002
## 440 0.4903 nan 0.0100 -0.0001
## 460 0.4794 nan 0.0100 0.0000
## 480 0.4699 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3111 nan 0.0100 0.0043
## 2 1.3030 nan 0.0100 0.0034
## 3 1.2950 nan 0.0100 0.0038
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2775 nan 0.0100 0.0035
## 6 1.2688 nan 0.0100 0.0038
## 7 1.2610 nan 0.0100 0.0037
## 8 1.2532 nan 0.0100 0.0033
## 9 1.2455 nan 0.0100 0.0036
## 10 1.2374 nan 0.0100 0.0037
## 20 1.1649 nan 0.0100 0.0030
## 40 1.0516 nan 0.0100 0.0020
## 60 0.9633 nan 0.0100 0.0015
## 80 0.8936 nan 0.0100 0.0013
## 100 0.8396 nan 0.0100 0.0007
## 120 0.7955 nan 0.0100 0.0008
## 140 0.7576 nan 0.0100 0.0004
## 160 0.7269 nan 0.0100 0.0003
## 180 0.6992 nan 0.0100 0.0001
## 200 0.6766 nan 0.0100 0.0001
## 220 0.6546 nan 0.0100 0.0000
## 240 0.6378 nan 0.0100 -0.0001
## 260 0.6203 nan 0.0100 0.0001
## 280 0.6049 nan 0.0100 0.0002
## 300 0.5900 nan 0.0100 0.0000
## 320 0.5762 nan 0.0100 0.0001
## 340 0.5622 nan 0.0100 -0.0000
## 360 0.5495 nan 0.0100 -0.0000
## 380 0.5372 nan 0.0100 0.0000
## 400 0.5250 nan 0.0100 -0.0000
## 420 0.5130 nan 0.0100 -0.0000
## 440 0.5020 nan 0.0100 0.0000
## 460 0.4911 nan 0.0100 0.0000
## 480 0.4808 nan 0.0100 -0.0000
## 500 0.4709 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0041
## 2 1.3033 nan 0.0100 0.0039
## 3 1.2943 nan 0.0100 0.0040
## 4 1.2853 nan 0.0100 0.0039
## 5 1.2764 nan 0.0100 0.0040
## 6 1.2679 nan 0.0100 0.0040
## 7 1.2599 nan 0.0100 0.0032
## 8 1.2519 nan 0.0100 0.0037
## 9 1.2440 nan 0.0100 0.0033
## 10 1.2362 nan 0.0100 0.0036
## 20 1.1650 nan 0.0100 0.0029
## 40 1.0533 nan 0.0100 0.0019
## 60 0.9671 nan 0.0100 0.0016
## 80 0.9022 nan 0.0100 0.0010
## 100 0.8470 nan 0.0100 0.0010
## 120 0.8027 nan 0.0100 0.0007
## 140 0.7661 nan 0.0100 0.0005
## 160 0.7356 nan 0.0100 0.0004
## 180 0.7083 nan 0.0100 0.0003
## 200 0.6851 nan 0.0100 0.0003
## 220 0.6647 nan 0.0100 0.0000
## 240 0.6458 nan 0.0100 0.0000
## 260 0.6284 nan 0.0100 0.0001
## 280 0.6124 nan 0.0100 0.0001
## 300 0.5969 nan 0.0100 0.0000
## 320 0.5812 nan 0.0100 0.0000
## 340 0.5674 nan 0.0100 -0.0000
## 360 0.5548 nan 0.0100 -0.0001
## 380 0.5433 nan 0.0100 -0.0000
## 400 0.5323 nan 0.0100 -0.0001
## 420 0.5214 nan 0.0100 -0.0001
## 440 0.5104 nan 0.0100 -0.0002
## 460 0.5007 nan 0.0100 0.0000
## 480 0.4913 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2403 nan 0.1000 0.0394
## 2 1.1743 nan 0.1000 0.0312
## 3 1.1206 nan 0.1000 0.0246
## 4 1.0750 nan 0.1000 0.0193
## 5 1.0319 nan 0.1000 0.0182
## 6 0.9978 nan 0.1000 0.0139
## 7 0.9649 nan 0.1000 0.0126
## 8 0.9335 nan 0.1000 0.0115
## 9 0.9061 nan 0.1000 0.0103
## 10 0.8819 nan 0.1000 0.0089
## 20 0.7356 nan 0.1000 0.0033
## 40 0.6047 nan 0.1000 -0.0006
## 60 0.5215 nan 0.1000 -0.0015
## 80 0.4611 nan 0.1000 -0.0011
## 100 0.4097 nan 0.1000 0.0003
## 120 0.3647 nan 0.1000 -0.0004
## 140 0.3256 nan 0.1000 -0.0010
## 160 0.2962 nan 0.1000 -0.0015
## 180 0.2685 nan 0.1000 -0.0002
## 200 0.2436 nan 0.1000 -0.0002
## 220 0.2213 nan 0.1000 -0.0005
## 240 0.2030 nan 0.1000 -0.0006
## 260 0.1868 nan 0.1000 -0.0004
## 280 0.1725 nan 0.1000 -0.0002
## 300 0.1588 nan 0.1000 -0.0003
## 320 0.1458 nan 0.1000 -0.0002
## 340 0.1346 nan 0.1000 -0.0002
## 360 0.1227 nan 0.1000 -0.0001
## 380 0.1134 nan 0.1000 -0.0003
## 400 0.1043 nan 0.1000 0.0002
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## 440 0.0888 nan 0.1000 -0.0004
## 460 0.0820 nan 0.1000 -0.0000
## 480 0.0758 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2509 nan 0.1000 0.0334
## 2 1.1864 nan 0.1000 0.0265
## 3 1.1247 nan 0.1000 0.0266
## 4 1.0757 nan 0.1000 0.0211
## 5 1.0304 nan 0.1000 0.0198
## 6 0.9920 nan 0.1000 0.0164
## 7 0.9586 nan 0.1000 0.0097
## 8 0.9312 nan 0.1000 0.0104
## 9 0.9080 nan 0.1000 0.0079
## 10 0.8838 nan 0.1000 0.0092
## 20 0.7409 nan 0.1000 0.0001
## 40 0.6120 nan 0.1000 -0.0013
## 60 0.5359 nan 0.1000 -0.0017
## 80 0.4693 nan 0.1000 -0.0009
## 100 0.4200 nan 0.1000 -0.0011
## 120 0.3749 nan 0.1000 -0.0011
## 140 0.3340 nan 0.1000 -0.0002
## 160 0.3011 nan 0.1000 -0.0004
## 180 0.2701 nan 0.1000 -0.0006
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## 220 0.2284 nan 0.1000 -0.0010
## 240 0.2086 nan 0.1000 -0.0007
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## 280 0.1757 nan 0.1000 -0.0003
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## 380 0.1172 nan 0.1000 -0.0003
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## 480 0.0788 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.1000 0.0371
## 2 1.1699 nan 0.1000 0.0306
## 3 1.1148 nan 0.1000 0.0245
## 4 1.0653 nan 0.1000 0.0239
## 5 1.0270 nan 0.1000 0.0159
## 6 0.9959 nan 0.1000 0.0146
## 7 0.9591 nan 0.1000 0.0152
## 8 0.9299 nan 0.1000 0.0109
## 9 0.9045 nan 0.1000 0.0115
## 10 0.8811 nan 0.1000 0.0076
## 20 0.7402 nan 0.1000 0.0020
## 40 0.6166 nan 0.1000 0.0006
## 60 0.5365 nan 0.1000 -0.0014
## 80 0.4775 nan 0.1000 -0.0010
## 100 0.4262 nan 0.1000 -0.0007
## 120 0.3816 nan 0.1000 -0.0012
## 140 0.3423 nan 0.1000 -0.0006
## 160 0.3105 nan 0.1000 -0.0002
## 180 0.2838 nan 0.1000 -0.0025
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## 240 0.2146 nan 0.1000 -0.0004
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## 320 0.1544 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2240 nan 0.1000 0.0416
## 2 1.1543 nan 0.1000 0.0325
## 3 1.0932 nan 0.1000 0.0246
## 4 1.0470 nan 0.1000 0.0190
## 5 1.0070 nan 0.1000 0.0153
## 6 0.9696 nan 0.1000 0.0177
## 7 0.9321 nan 0.1000 0.0131
## 8 0.9023 nan 0.1000 0.0113
## 9 0.8739 nan 0.1000 0.0121
## 10 0.8479 nan 0.1000 0.0099
## 20 0.6962 nan 0.1000 0.0026
## 40 0.5499 nan 0.1000 0.0006
## 60 0.4632 nan 0.1000 -0.0001
## 80 0.3970 nan 0.1000 -0.0007
## 100 0.3441 nan 0.1000 -0.0005
## 120 0.3019 nan 0.1000 -0.0006
## 140 0.2683 nan 0.1000 -0.0006
## 160 0.2363 nan 0.1000 -0.0014
## 180 0.2111 nan 0.1000 -0.0014
## 200 0.1883 nan 0.1000 0.0001
## 220 0.1645 nan 0.1000 -0.0004
## 240 0.1472 nan 0.1000 -0.0003
## 260 0.1319 nan 0.1000 -0.0002
## 280 0.1177 nan 0.1000 -0.0001
## 300 0.1064 nan 0.1000 -0.0005
## 320 0.0968 nan 0.1000 -0.0002
## 340 0.0870 nan 0.1000 -0.0003
## 360 0.0782 nan 0.1000 -0.0000
## 380 0.0711 nan 0.1000 -0.0002
## 400 0.0641 nan 0.1000 -0.0001
## 420 0.0588 nan 0.1000 0.0000
## 440 0.0529 nan 0.1000 -0.0000
## 460 0.0485 nan 0.1000 -0.0001
## 480 0.0436 nan 0.1000 0.0000
## 500 0.0395 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0387
## 2 1.1704 nan 0.1000 0.0278
## 3 1.1123 nan 0.1000 0.0270
## 4 1.0574 nan 0.1000 0.0219
## 5 1.0140 nan 0.1000 0.0174
## 6 0.9747 nan 0.1000 0.0181
## 7 0.9440 nan 0.1000 0.0106
## 8 0.9158 nan 0.1000 0.0102
## 9 0.8895 nan 0.1000 0.0076
## 10 0.8649 nan 0.1000 0.0092
## 20 0.7068 nan 0.1000 0.0024
## 40 0.5659 nan 0.1000 -0.0015
## 60 0.4757 nan 0.1000 -0.0008
## 80 0.4002 nan 0.1000 -0.0015
## 100 0.3511 nan 0.1000 -0.0009
## 120 0.3024 nan 0.1000 -0.0004
## 140 0.2657 nan 0.1000 -0.0010
## 160 0.2384 nan 0.1000 -0.0005
## 180 0.2101 nan 0.1000 -0.0011
## 200 0.1884 nan 0.1000 -0.0007
## 220 0.1676 nan 0.1000 -0.0006
## 240 0.1497 nan 0.1000 -0.0010
## 260 0.1351 nan 0.1000 -0.0006
## 280 0.1207 nan 0.1000 -0.0003
## 300 0.1095 nan 0.1000 -0.0007
## 320 0.0987 nan 0.1000 -0.0003
## 340 0.0892 nan 0.1000 -0.0005
## 360 0.0813 nan 0.1000 -0.0002
## 380 0.0734 nan 0.1000 -0.0004
## 400 0.0671 nan 0.1000 -0.0002
## 420 0.0614 nan 0.1000 -0.0002
## 440 0.0557 nan 0.1000 -0.0003
## 460 0.0504 nan 0.1000 -0.0002
## 480 0.0459 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0362
## 2 1.1707 nan 0.1000 0.0342
## 3 1.1115 nan 0.1000 0.0264
## 4 1.0620 nan 0.1000 0.0233
## 5 1.0164 nan 0.1000 0.0217
## 6 0.9767 nan 0.1000 0.0165
## 7 0.9401 nan 0.1000 0.0141
## 8 0.9081 nan 0.1000 0.0113
## 9 0.8805 nan 0.1000 0.0121
## 10 0.8580 nan 0.1000 0.0084
## 20 0.7049 nan 0.1000 0.0035
## 40 0.5691 nan 0.1000 0.0000
## 60 0.4878 nan 0.1000 -0.0007
## 80 0.4201 nan 0.1000 0.0001
## 100 0.3689 nan 0.1000 -0.0017
## 120 0.3226 nan 0.1000 -0.0012
## 140 0.2825 nan 0.1000 -0.0004
## 160 0.2475 nan 0.1000 -0.0005
## 180 0.2191 nan 0.1000 -0.0003
## 200 0.1944 nan 0.1000 -0.0007
## 220 0.1735 nan 0.1000 -0.0002
## 240 0.1563 nan 0.1000 -0.0009
## 260 0.1401 nan 0.1000 -0.0006
## 280 0.1277 nan 0.1000 -0.0001
## 300 0.1149 nan 0.1000 -0.0004
## 320 0.1047 nan 0.1000 -0.0003
## 340 0.0944 nan 0.1000 -0.0004
## 360 0.0852 nan 0.1000 -0.0002
## 380 0.0772 nan 0.1000 -0.0003
## 400 0.0697 nan 0.1000 -0.0003
## 420 0.0637 nan 0.1000 -0.0003
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## 460 0.0535 nan 0.1000 -0.0002
## 480 0.0486 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2295 nan 0.1000 0.0426
## 2 1.1476 nan 0.1000 0.0343
## 3 1.0883 nan 0.1000 0.0260
## 4 1.0358 nan 0.1000 0.0209
## 5 0.9949 nan 0.1000 0.0174
## 6 0.9568 nan 0.1000 0.0160
## 7 0.9169 nan 0.1000 0.0151
## 8 0.8862 nan 0.1000 0.0135
## 9 0.8568 nan 0.1000 0.0125
## 10 0.8307 nan 0.1000 0.0074
## 20 0.6742 nan 0.1000 0.0003
## 40 0.5148 nan 0.1000 -0.0013
## 60 0.4146 nan 0.1000 0.0005
## 80 0.3489 nan 0.1000 -0.0006
## 100 0.2939 nan 0.1000 -0.0013
## 120 0.2480 nan 0.1000 -0.0012
## 140 0.2121 nan 0.1000 -0.0001
## 160 0.1834 nan 0.1000 -0.0001
## 180 0.1580 nan 0.1000 -0.0004
## 200 0.1371 nan 0.1000 -0.0001
## 220 0.1170 nan 0.1000 -0.0002
## 240 0.1032 nan 0.1000 -0.0001
## 260 0.0917 nan 0.1000 -0.0003
## 280 0.0816 nan 0.1000 0.0000
## 300 0.0726 nan 0.1000 -0.0002
## 320 0.0637 nan 0.1000 -0.0001
## 340 0.0560 nan 0.1000 -0.0002
## 360 0.0498 nan 0.1000 -0.0001
## 380 0.0441 nan 0.1000 -0.0000
## 400 0.0394 nan 0.1000 -0.0000
## 420 0.0353 nan 0.1000 -0.0000
## 440 0.0313 nan 0.1000 -0.0001
## 460 0.0278 nan 0.1000 -0.0001
## 480 0.0251 nan 0.1000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2303 nan 0.1000 0.0356
## 2 1.1605 nan 0.1000 0.0299
## 3 1.0959 nan 0.1000 0.0302
## 4 1.0394 nan 0.1000 0.0224
## 5 0.9968 nan 0.1000 0.0187
## 6 0.9569 nan 0.1000 0.0177
## 7 0.9254 nan 0.1000 0.0114
## 8 0.8951 nan 0.1000 0.0112
## 9 0.8689 nan 0.1000 0.0094
## 10 0.8404 nan 0.1000 0.0106
## 20 0.6839 nan 0.1000 0.0009
## 40 0.5214 nan 0.1000 0.0004
## 60 0.4227 nan 0.1000 -0.0018
## 80 0.3522 nan 0.1000 -0.0008
## 100 0.3014 nan 0.1000 0.0001
## 120 0.2602 nan 0.1000 -0.0004
## 140 0.2163 nan 0.1000 -0.0006
## 160 0.1862 nan 0.1000 -0.0009
## 180 0.1606 nan 0.1000 0.0000
## 200 0.1407 nan 0.1000 -0.0008
## 220 0.1222 nan 0.1000 -0.0002
## 240 0.1068 nan 0.1000 -0.0001
## 260 0.0941 nan 0.1000 -0.0005
## 280 0.0829 nan 0.1000 -0.0001
## 300 0.0730 nan 0.1000 -0.0002
## 320 0.0631 nan 0.1000 -0.0002
## 340 0.0563 nan 0.1000 -0.0002
## 360 0.0500 nan 0.1000 -0.0002
## 380 0.0442 nan 0.1000 -0.0002
## 400 0.0398 nan 0.1000 -0.0001
## 420 0.0353 nan 0.1000 -0.0002
## 440 0.0312 nan 0.1000 -0.0001
## 460 0.0279 nan 0.1000 -0.0001
## 480 0.0252 nan 0.1000 -0.0001
## 500 0.0226 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2321 nan 0.1000 0.0411
## 2 1.1604 nan 0.1000 0.0315
## 3 1.1016 nan 0.1000 0.0271
## 4 1.0512 nan 0.1000 0.0221
## 5 1.0049 nan 0.1000 0.0191
## 6 0.9673 nan 0.1000 0.0161
## 7 0.9309 nan 0.1000 0.0152
## 8 0.9066 nan 0.1000 0.0077
## 9 0.8819 nan 0.1000 0.0096
## 10 0.8556 nan 0.1000 0.0104
## 20 0.6852 nan 0.1000 0.0013
## 40 0.5429 nan 0.1000 0.0005
## 60 0.4519 nan 0.1000 -0.0017
## 80 0.3700 nan 0.1000 -0.0013
## 100 0.3108 nan 0.1000 -0.0003
## 120 0.2650 nan 0.1000 -0.0012
## 140 0.2295 nan 0.1000 -0.0010
## 160 0.2020 nan 0.1000 -0.0009
## 180 0.1750 nan 0.1000 -0.0008
## 200 0.1515 nan 0.1000 -0.0000
## 220 0.1328 nan 0.1000 -0.0003
## 240 0.1171 nan 0.1000 -0.0005
## 260 0.1030 nan 0.1000 -0.0004
## 280 0.0904 nan 0.1000 -0.0005
## 300 0.0808 nan 0.1000 -0.0001
## 320 0.0725 nan 0.1000 -0.0003
## 340 0.0647 nan 0.1000 -0.0002
## 360 0.0580 nan 0.1000 -0.0003
## 380 0.0519 nan 0.1000 -0.0004
## 400 0.0456 nan 0.1000 -0.0001
## 420 0.0408 nan 0.1000 -0.0001
## 440 0.0364 nan 0.1000 -0.0003
## 460 0.0330 nan 0.1000 -0.0001
## 480 0.0299 nan 0.1000 -0.0000
## 500 0.0268 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2697 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0004
## 100 1.2386 nan 0.0010 0.0003
## 120 1.2237 nan 0.0010 0.0003
## 140 1.2093 nan 0.0010 0.0003
## 160 1.1954 nan 0.0010 0.0003
## 180 1.1820 nan 0.0010 0.0003
## 200 1.1691 nan 0.0010 0.0003
## 220 1.1561 nan 0.0010 0.0003
## 240 1.1437 nan 0.0010 0.0003
## 260 1.1317 nan 0.0010 0.0003
## 280 1.1200 nan 0.0010 0.0002
## 300 1.1086 nan 0.0010 0.0003
## 320 1.0977 nan 0.0010 0.0002
## 340 1.0870 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0665 nan 0.0010 0.0002
## 400 1.0565 nan 0.0010 0.0002
## 420 1.0469 nan 0.0010 0.0002
## 440 1.0374 nan 0.0010 0.0002
## 460 1.0283 nan 0.0010 0.0002
## 480 1.0195 nan 0.0010 0.0002
## 500 1.0107 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3148 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3122 nan 0.0010 0.0003
## 20 1.3033 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2543 nan 0.0010 0.0004
## 100 1.2389 nan 0.0010 0.0003
## 120 1.2236 nan 0.0010 0.0003
## 140 1.2092 nan 0.0010 0.0003
## 160 1.1952 nan 0.0010 0.0003
## 180 1.1817 nan 0.0010 0.0003
## 200 1.1685 nan 0.0010 0.0003
## 220 1.1560 nan 0.0010 0.0003
## 240 1.1437 nan 0.0010 0.0003
## 260 1.1319 nan 0.0010 0.0002
## 280 1.1200 nan 0.0010 0.0003
## 300 1.1087 nan 0.0010 0.0003
## 320 1.0976 nan 0.0010 0.0002
## 340 1.0868 nan 0.0010 0.0002
## 360 1.0766 nan 0.0010 0.0002
## 380 1.0664 nan 0.0010 0.0002
## 400 1.0565 nan 0.0010 0.0002
## 420 1.0469 nan 0.0010 0.0002
## 440 1.0377 nan 0.0010 0.0002
## 460 1.0288 nan 0.0010 0.0002
## 480 1.0198 nan 0.0010 0.0002
## 500 1.0110 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3124 nan 0.0010 0.0004
## 20 1.3038 nan 0.0010 0.0004
## 40 1.2871 nan 0.0010 0.0004
## 60 1.2710 nan 0.0010 0.0004
## 80 1.2554 nan 0.0010 0.0003
## 100 1.2403 nan 0.0010 0.0003
## 120 1.2259 nan 0.0010 0.0003
## 140 1.2117 nan 0.0010 0.0004
## 160 1.1977 nan 0.0010 0.0003
## 180 1.1845 nan 0.0010 0.0003
## 200 1.1714 nan 0.0010 0.0003
## 220 1.1585 nan 0.0010 0.0003
## 240 1.1461 nan 0.0010 0.0003
## 260 1.1342 nan 0.0010 0.0002
## 280 1.1228 nan 0.0010 0.0003
## 300 1.1115 nan 0.0010 0.0002
## 320 1.1006 nan 0.0010 0.0002
## 340 1.0901 nan 0.0010 0.0002
## 360 1.0798 nan 0.0010 0.0002
## 380 1.0697 nan 0.0010 0.0002
## 400 1.0597 nan 0.0010 0.0002
## 420 1.0501 nan 0.0010 0.0002
## 440 1.0409 nan 0.0010 0.0002
## 460 1.0320 nan 0.0010 0.0002
## 480 1.0230 nan 0.0010 0.0002
## 500 1.0143 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0005
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2677 nan 0.0010 0.0004
## 80 1.2512 nan 0.0010 0.0004
## 100 1.2348 nan 0.0010 0.0004
## 120 1.2188 nan 0.0010 0.0003
## 140 1.2034 nan 0.0010 0.0004
## 160 1.1886 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1220 nan 0.0010 0.0003
## 280 1.1096 nan 0.0010 0.0003
## 300 1.0977 nan 0.0010 0.0003
## 320 1.0861 nan 0.0010 0.0003
## 340 1.0746 nan 0.0010 0.0002
## 360 1.0635 nan 0.0010 0.0003
## 380 1.0528 nan 0.0010 0.0002
## 400 1.0423 nan 0.0010 0.0002
## 420 1.0322 nan 0.0010 0.0002
## 440 1.0224 nan 0.0010 0.0002
## 460 1.0126 nan 0.0010 0.0002
## 480 1.0033 nan 0.0010 0.0002
## 500 0.9941 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2848 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2508 nan 0.0010 0.0004
## 100 1.2347 nan 0.0010 0.0003
## 120 1.2190 nan 0.0010 0.0003
## 140 1.2038 nan 0.0010 0.0003
## 160 1.1890 nan 0.0010 0.0003
## 180 1.1747 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1473 nan 0.0010 0.0003
## 240 1.1344 nan 0.0010 0.0003
## 260 1.1221 nan 0.0010 0.0002
## 280 1.1099 nan 0.0010 0.0003
## 300 1.0979 nan 0.0010 0.0002
## 320 1.0862 nan 0.0010 0.0002
## 340 1.0746 nan 0.0010 0.0002
## 360 1.0635 nan 0.0010 0.0002
## 380 1.0530 nan 0.0010 0.0002
## 400 1.0426 nan 0.0010 0.0002
## 420 1.0325 nan 0.0010 0.0002
## 440 1.0228 nan 0.0010 0.0002
## 460 1.0133 nan 0.0010 0.0002
## 480 1.0039 nan 0.0010 0.0002
## 500 0.9948 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0004
## 7 1.3147 nan 0.0010 0.0004
## 8 1.3138 nan 0.0010 0.0004
## 9 1.3129 nan 0.0010 0.0004
## 10 1.3120 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2852 nan 0.0010 0.0004
## 60 1.2680 nan 0.0010 0.0003
## 80 1.2515 nan 0.0010 0.0003
## 100 1.2355 nan 0.0010 0.0004
## 120 1.2201 nan 0.0010 0.0004
## 140 1.2049 nan 0.0010 0.0003
## 160 1.1904 nan 0.0010 0.0003
## 180 1.1764 nan 0.0010 0.0003
## 200 1.1628 nan 0.0010 0.0003
## 220 1.1493 nan 0.0010 0.0003
## 240 1.1364 nan 0.0010 0.0003
## 260 1.1241 nan 0.0010 0.0003
## 280 1.1119 nan 0.0010 0.0003
## 300 1.1002 nan 0.0010 0.0003
## 320 1.0889 nan 0.0010 0.0003
## 340 1.0779 nan 0.0010 0.0002
## 360 1.0669 nan 0.0010 0.0002
## 380 1.0565 nan 0.0010 0.0003
## 400 1.0460 nan 0.0010 0.0002
## 420 1.0360 nan 0.0010 0.0002
## 440 1.0260 nan 0.0010 0.0002
## 460 1.0162 nan 0.0010 0.0002
## 480 1.0072 nan 0.0010 0.0002
## 500 0.9980 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3124 nan 0.0010 0.0004
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0005
## 40 1.2830 nan 0.0010 0.0004
## 60 1.2646 nan 0.0010 0.0005
## 80 1.2467 nan 0.0010 0.0004
## 100 1.2300 nan 0.0010 0.0003
## 120 1.2134 nan 0.0010 0.0003
## 140 1.1974 nan 0.0010 0.0003
## 160 1.1820 nan 0.0010 0.0003
## 180 1.1672 nan 0.0010 0.0004
## 200 1.1528 nan 0.0010 0.0003
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## 240 1.1251 nan 0.0010 0.0003
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## 280 1.0992 nan 0.0010 0.0003
## 300 1.0869 nan 0.0010 0.0003
## 320 1.0746 nan 0.0010 0.0002
## 340 1.0630 nan 0.0010 0.0002
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## 380 1.0407 nan 0.0010 0.0002
## 400 1.0296 nan 0.0010 0.0002
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## 460 0.9989 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3154 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0005
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0005
## 40 1.2832 nan 0.0010 0.0004
## 60 1.2652 nan 0.0010 0.0004
## 80 1.2477 nan 0.0010 0.0004
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## 120 1.2147 nan 0.0010 0.0004
## 140 1.1990 nan 0.0010 0.0004
## 160 1.1836 nan 0.0010 0.0004
## 180 1.1687 nan 0.0010 0.0003
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## 320 1.0767 nan 0.0010 0.0003
## 340 1.0651 nan 0.0010 0.0002
## 360 1.0539 nan 0.0010 0.0002
## 380 1.0429 nan 0.0010 0.0002
## 400 1.0320 nan 0.0010 0.0002
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## 440 1.0112 nan 0.0010 0.0002
## 460 1.0011 nan 0.0010 0.0002
## 480 0.9915 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0005
## 6 1.3155 nan 0.0010 0.0005
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0005
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## 10 1.3115 nan 0.0010 0.0005
## 20 1.3019 nan 0.0010 0.0004
## 40 1.2838 nan 0.0010 0.0004
## 60 1.2659 nan 0.0010 0.0004
## 80 1.2485 nan 0.0010 0.0004
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## 120 1.2156 nan 0.0010 0.0004
## 140 1.2001 nan 0.0010 0.0003
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## 380 1.0471 nan 0.0010 0.0002
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## 460 1.0064 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3118 nan 0.0100 0.0044
## 2 1.3035 nan 0.0100 0.0036
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2864 nan 0.0100 0.0040
## 5 1.2779 nan 0.0100 0.0040
## 6 1.2694 nan 0.0100 0.0039
## 7 1.2612 nan 0.0100 0.0036
## 8 1.2535 nan 0.0100 0.0034
## 9 1.2462 nan 0.0100 0.0033
## 10 1.2387 nan 0.0100 0.0034
## 20 1.1676 nan 0.0100 0.0026
## 40 1.0537 nan 0.0100 0.0023
## 60 0.9691 nan 0.0100 0.0014
## 80 0.9022 nan 0.0100 0.0013
## 100 0.8471 nan 0.0100 0.0010
## 120 0.8030 nan 0.0100 0.0008
## 140 0.7665 nan 0.0100 0.0005
## 160 0.7357 nan 0.0100 0.0004
## 180 0.7087 nan 0.0100 0.0004
## 200 0.6849 nan 0.0100 0.0002
## 220 0.6633 nan 0.0100 0.0001
## 240 0.6445 nan 0.0100 0.0001
## 260 0.6290 nan 0.0100 0.0003
## 280 0.6139 nan 0.0100 0.0001
## 300 0.5991 nan 0.0100 0.0002
## 320 0.5866 nan 0.0100 0.0000
## 340 0.5747 nan 0.0100 0.0001
## 360 0.5637 nan 0.0100 0.0001
## 380 0.5525 nan 0.0100 -0.0000
## 400 0.5420 nan 0.0100 0.0001
## 420 0.5324 nan 0.0100 0.0001
## 440 0.5232 nan 0.0100 -0.0000
## 460 0.5146 nan 0.0100 0.0000
## 480 0.5059 nan 0.0100 -0.0001
## 500 0.4972 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3123 nan 0.0100 0.0041
## 2 1.3026 nan 0.0100 0.0044
## 3 1.2943 nan 0.0100 0.0039
## 4 1.2858 nan 0.0100 0.0039
## 5 1.2771 nan 0.0100 0.0038
## 6 1.2685 nan 0.0100 0.0038
## 7 1.2607 nan 0.0100 0.0037
## 8 1.2526 nan 0.0100 0.0033
## 9 1.2451 nan 0.0100 0.0031
## 10 1.2374 nan 0.0100 0.0036
## 20 1.1691 nan 0.0100 0.0029
## 40 1.0574 nan 0.0100 0.0020
## 60 0.9704 nan 0.0100 0.0015
## 80 0.9026 nan 0.0100 0.0012
## 100 0.8494 nan 0.0100 0.0009
## 120 0.8056 nan 0.0100 0.0007
## 140 0.7699 nan 0.0100 0.0005
## 160 0.7403 nan 0.0100 0.0004
## 180 0.7136 nan 0.0100 0.0004
## 200 0.6903 nan 0.0100 0.0003
## 220 0.6701 nan 0.0100 0.0003
## 240 0.6519 nan 0.0100 0.0002
## 260 0.6360 nan 0.0100 0.0001
## 280 0.6219 nan 0.0100 0.0001
## 300 0.6095 nan 0.0100 0.0002
## 320 0.5975 nan 0.0100 0.0001
## 340 0.5855 nan 0.0100 -0.0001
## 360 0.5750 nan 0.0100 0.0001
## 380 0.5638 nan 0.0100 -0.0000
## 400 0.5548 nan 0.0100 0.0000
## 420 0.5444 nan 0.0100 -0.0000
## 440 0.5361 nan 0.0100 -0.0001
## 460 0.5280 nan 0.0100 -0.0001
## 480 0.5185 nan 0.0100 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3126 nan 0.0100 0.0040
## 2 1.3039 nan 0.0100 0.0042
## 3 1.2952 nan 0.0100 0.0040
## 4 1.2862 nan 0.0100 0.0042
## 5 1.2775 nan 0.0100 0.0037
## 6 1.2698 nan 0.0100 0.0037
## 7 1.2618 nan 0.0100 0.0035
## 8 1.2540 nan 0.0100 0.0036
## 9 1.2465 nan 0.0100 0.0035
## 10 1.2392 nan 0.0100 0.0033
## 20 1.1698 nan 0.0100 0.0029
## 40 1.0588 nan 0.0100 0.0018
## 60 0.9730 nan 0.0100 0.0016
## 80 0.9044 nan 0.0100 0.0012
## 100 0.8525 nan 0.0100 0.0007
## 120 0.8087 nan 0.0100 0.0008
## 140 0.7723 nan 0.0100 0.0006
## 160 0.7420 nan 0.0100 0.0004
## 180 0.7160 nan 0.0100 0.0002
## 200 0.6940 nan 0.0100 0.0002
## 220 0.6739 nan 0.0100 0.0003
## 240 0.6560 nan 0.0100 0.0002
## 260 0.6403 nan 0.0100 0.0000
## 280 0.6265 nan 0.0100 0.0002
## 300 0.6133 nan 0.0100 0.0000
## 320 0.6014 nan 0.0100 -0.0002
## 340 0.5901 nan 0.0100 0.0001
## 360 0.5790 nan 0.0100 -0.0000
## 380 0.5693 nan 0.0100 0.0000
## 400 0.5596 nan 0.0100 0.0002
## 420 0.5501 nan 0.0100 0.0001
## 440 0.5418 nan 0.0100 0.0000
## 460 0.5333 nan 0.0100 -0.0001
## 480 0.5252 nan 0.0100 -0.0002
## 500 0.5183 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0044
## 2 1.3016 nan 0.0100 0.0042
## 3 1.2931 nan 0.0100 0.0039
## 4 1.2844 nan 0.0100 0.0039
## 5 1.2756 nan 0.0100 0.0043
## 6 1.2674 nan 0.0100 0.0038
## 7 1.2587 nan 0.0100 0.0040
## 8 1.2500 nan 0.0100 0.0035
## 9 1.2417 nan 0.0100 0.0034
## 10 1.2339 nan 0.0100 0.0033
## 20 1.1583 nan 0.0100 0.0034
## 40 1.0397 nan 0.0100 0.0025
## 60 0.9479 nan 0.0100 0.0018
## 80 0.8779 nan 0.0100 0.0013
## 100 0.8224 nan 0.0100 0.0007
## 120 0.7765 nan 0.0100 0.0005
## 140 0.7366 nan 0.0100 0.0004
## 160 0.7057 nan 0.0100 0.0002
## 180 0.6769 nan 0.0100 0.0004
## 200 0.6525 nan 0.0100 0.0001
## 220 0.6304 nan 0.0100 0.0002
## 240 0.6100 nan 0.0100 0.0002
## 260 0.5921 nan 0.0100 0.0001
## 280 0.5762 nan 0.0100 0.0002
## 300 0.5611 nan 0.0100 0.0001
## 320 0.5464 nan 0.0100 0.0001
## 340 0.5329 nan 0.0100 0.0000
## 360 0.5210 nan 0.0100 0.0000
## 380 0.5100 nan 0.0100 -0.0000
## 400 0.4990 nan 0.0100 -0.0001
## 420 0.4883 nan 0.0100 -0.0000
## 440 0.4792 nan 0.0100 -0.0000
## 460 0.4694 nan 0.0100 -0.0001
## 480 0.4607 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3022 nan 0.0100 0.0043
## 3 1.2932 nan 0.0100 0.0041
## 4 1.2843 nan 0.0100 0.0044
## 5 1.2752 nan 0.0100 0.0039
## 6 1.2672 nan 0.0100 0.0040
## 7 1.2589 nan 0.0100 0.0035
## 8 1.2511 nan 0.0100 0.0035
## 9 1.2428 nan 0.0100 0.0037
## 10 1.2345 nan 0.0100 0.0032
## 20 1.1602 nan 0.0100 0.0029
## 40 1.0420 nan 0.0100 0.0021
## 60 0.9519 nan 0.0100 0.0019
## 80 0.8819 nan 0.0100 0.0010
## 100 0.8250 nan 0.0100 0.0009
## 120 0.7786 nan 0.0100 0.0008
## 140 0.7401 nan 0.0100 0.0006
## 160 0.7096 nan 0.0100 0.0003
## 180 0.6804 nan 0.0100 0.0004
## 200 0.6566 nan 0.0100 0.0004
## 220 0.6357 nan 0.0100 0.0003
## 240 0.6166 nan 0.0100 0.0002
## 260 0.5997 nan 0.0100 0.0001
## 280 0.5840 nan 0.0100 0.0000
## 300 0.5677 nan 0.0100 -0.0000
## 320 0.5537 nan 0.0100 0.0001
## 340 0.5415 nan 0.0100 -0.0002
## 360 0.5290 nan 0.0100 0.0001
## 380 0.5182 nan 0.0100 0.0001
## 400 0.5075 nan 0.0100 0.0000
## 420 0.4975 nan 0.0100 -0.0000
## 440 0.4873 nan 0.0100 -0.0000
## 460 0.4777 nan 0.0100 -0.0002
## 480 0.4687 nan 0.0100 0.0001
## 500 0.4590 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3026 nan 0.0100 0.0045
## 3 1.2936 nan 0.0100 0.0038
## 4 1.2844 nan 0.0100 0.0038
## 5 1.2757 nan 0.0100 0.0040
## 6 1.2672 nan 0.0100 0.0038
## 7 1.2583 nan 0.0100 0.0040
## 8 1.2501 nan 0.0100 0.0037
## 9 1.2423 nan 0.0100 0.0038
## 10 1.2340 nan 0.0100 0.0038
## 20 1.1616 nan 0.0100 0.0029
## 40 1.0460 nan 0.0100 0.0019
## 60 0.9583 nan 0.0100 0.0017
## 80 0.8877 nan 0.0100 0.0014
## 100 0.8331 nan 0.0100 0.0009
## 120 0.7865 nan 0.0100 0.0006
## 140 0.7500 nan 0.0100 0.0005
## 160 0.7186 nan 0.0100 0.0006
## 180 0.6894 nan 0.0100 0.0004
## 200 0.6647 nan 0.0100 0.0002
## 220 0.6433 nan 0.0100 0.0003
## 240 0.6240 nan 0.0100 0.0002
## 260 0.6074 nan 0.0100 0.0002
## 280 0.5917 nan 0.0100 -0.0001
## 300 0.5776 nan 0.0100 0.0000
## 320 0.5631 nan 0.0100 -0.0000
## 340 0.5505 nan 0.0100 0.0000
## 360 0.5381 nan 0.0100 0.0000
## 380 0.5268 nan 0.0100 -0.0001
## 400 0.5170 nan 0.0100 -0.0000
## 420 0.5065 nan 0.0100 -0.0000
## 440 0.4968 nan 0.0100 -0.0000
## 460 0.4874 nan 0.0100 -0.0001
## 480 0.4779 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3021 nan 0.0100 0.0044
## 3 1.2921 nan 0.0100 0.0045
## 4 1.2826 nan 0.0100 0.0045
## 5 1.2728 nan 0.0100 0.0045
## 6 1.2636 nan 0.0100 0.0046
## 7 1.2542 nan 0.0100 0.0041
## 8 1.2460 nan 0.0100 0.0035
## 9 1.2378 nan 0.0100 0.0038
## 10 1.2294 nan 0.0100 0.0040
## 20 1.1529 nan 0.0100 0.0031
## 40 1.0303 nan 0.0100 0.0022
## 60 0.9361 nan 0.0100 0.0016
## 80 0.8619 nan 0.0100 0.0013
## 100 0.8022 nan 0.0100 0.0010
## 120 0.7527 nan 0.0100 0.0008
## 140 0.7131 nan 0.0100 0.0006
## 160 0.6779 nan 0.0100 0.0003
## 180 0.6477 nan 0.0100 0.0003
## 200 0.6229 nan 0.0100 0.0002
## 220 0.6004 nan 0.0100 0.0003
## 240 0.5786 nan 0.0100 0.0002
## 260 0.5607 nan 0.0100 0.0000
## 280 0.5438 nan 0.0100 -0.0000
## 300 0.5271 nan 0.0100 0.0001
## 320 0.5106 nan 0.0100 0.0001
## 340 0.4974 nan 0.0100 -0.0000
## 360 0.4841 nan 0.0100 0.0000
## 380 0.4721 nan 0.0100 0.0000
## 400 0.4609 nan 0.0100 -0.0000
## 420 0.4501 nan 0.0100 -0.0000
## 440 0.4394 nan 0.0100 0.0001
## 460 0.4292 nan 0.0100 0.0001
## 480 0.4189 nan 0.0100 0.0001
## 500 0.4093 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3110 nan 0.0100 0.0046
## 2 1.3018 nan 0.0100 0.0043
## 3 1.2918 nan 0.0100 0.0040
## 4 1.2829 nan 0.0100 0.0041
## 5 1.2736 nan 0.0100 0.0043
## 6 1.2645 nan 0.0100 0.0037
## 7 1.2561 nan 0.0100 0.0036
## 8 1.2475 nan 0.0100 0.0032
## 9 1.2387 nan 0.0100 0.0038
## 10 1.2299 nan 0.0100 0.0040
## 20 1.1550 nan 0.0100 0.0031
## 40 1.0315 nan 0.0100 0.0023
## 60 0.9367 nan 0.0100 0.0016
## 80 0.8641 nan 0.0100 0.0013
## 100 0.8061 nan 0.0100 0.0009
## 120 0.7586 nan 0.0100 0.0008
## 140 0.7188 nan 0.0100 0.0006
## 160 0.6834 nan 0.0100 0.0007
## 180 0.6535 nan 0.0100 0.0004
## 200 0.6278 nan 0.0100 0.0003
## 220 0.6069 nan 0.0100 0.0002
## 240 0.5871 nan 0.0100 0.0001
## 260 0.5674 nan 0.0100 -0.0000
## 280 0.5500 nan 0.0100 0.0000
## 300 0.5347 nan 0.0100 0.0001
## 320 0.5199 nan 0.0100 0.0003
## 340 0.5052 nan 0.0100 0.0001
## 360 0.4931 nan 0.0100 -0.0000
## 380 0.4801 nan 0.0100 0.0001
## 400 0.4690 nan 0.0100 -0.0000
## 420 0.4569 nan 0.0100 -0.0001
## 440 0.4456 nan 0.0100 -0.0002
## 460 0.4345 nan 0.0100 0.0001
## 480 0.4253 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0042
## 2 1.3030 nan 0.0100 0.0038
## 3 1.2936 nan 0.0100 0.0042
## 4 1.2852 nan 0.0100 0.0040
## 5 1.2763 nan 0.0100 0.0042
## 6 1.2675 nan 0.0100 0.0040
## 7 1.2589 nan 0.0100 0.0036
## 8 1.2500 nan 0.0100 0.0041
## 9 1.2419 nan 0.0100 0.0041
## 10 1.2341 nan 0.0100 0.0035
## 20 1.1581 nan 0.0100 0.0032
## 40 1.0362 nan 0.0100 0.0022
## 60 0.9449 nan 0.0100 0.0020
## 80 0.8733 nan 0.0100 0.0012
## 100 0.8161 nan 0.0100 0.0009
## 120 0.7689 nan 0.0100 0.0009
## 140 0.7289 nan 0.0100 0.0007
## 160 0.6948 nan 0.0100 0.0002
## 180 0.6669 nan 0.0100 0.0004
## 200 0.6403 nan 0.0100 0.0004
## 220 0.6171 nan 0.0100 0.0003
## 240 0.5962 nan 0.0100 0.0002
## 260 0.5772 nan 0.0100 0.0003
## 280 0.5599 nan 0.0100 -0.0000
## 300 0.5450 nan 0.0100 0.0001
## 320 0.5296 nan 0.0100 0.0001
## 340 0.5159 nan 0.0100 0.0001
## 360 0.5039 nan 0.0100 -0.0001
## 380 0.4915 nan 0.0100 -0.0001
## 400 0.4801 nan 0.0100 -0.0000
## 420 0.4690 nan 0.0100 -0.0002
## 440 0.4582 nan 0.0100 -0.0001
## 460 0.4475 nan 0.0100 -0.0001
## 480 0.4374 nan 0.0100 -0.0001
## 500 0.4271 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2314 nan 0.1000 0.0401
## 2 1.1605 nan 0.1000 0.0323
## 3 1.1031 nan 0.1000 0.0242
## 4 1.0488 nan 0.1000 0.0252
## 5 1.0018 nan 0.1000 0.0196
## 6 0.9617 nan 0.1000 0.0186
## 7 0.9263 nan 0.1000 0.0135
## 8 0.8936 nan 0.1000 0.0139
## 9 0.8667 nan 0.1000 0.0114
## 10 0.8392 nan 0.1000 0.0089
## 20 0.6774 nan 0.1000 0.0022
## 40 0.5462 nan 0.1000 0.0003
## 60 0.4580 nan 0.1000 -0.0003
## 80 0.3982 nan 0.1000 -0.0000
## 100 0.3466 nan 0.1000 -0.0003
## 120 0.3086 nan 0.1000 -0.0009
## 140 0.2725 nan 0.1000 -0.0003
## 160 0.2432 nan 0.1000 -0.0003
## 180 0.2205 nan 0.1000 0.0002
## 200 0.1967 nan 0.1000 -0.0001
## 220 0.1791 nan 0.1000 -0.0004
## 240 0.1616 nan 0.1000 -0.0001
## 260 0.1459 nan 0.1000 -0.0002
## 280 0.1338 nan 0.1000 -0.0004
## 300 0.1208 nan 0.1000 -0.0002
## 320 0.1120 nan 0.1000 -0.0002
## 340 0.1023 nan 0.1000 -0.0000
## 360 0.0937 nan 0.1000 -0.0003
## 380 0.0851 nan 0.1000 -0.0002
## 400 0.0779 nan 0.1000 -0.0002
## 420 0.0720 nan 0.1000 -0.0003
## 440 0.0660 nan 0.1000 -0.0000
## 460 0.0604 nan 0.1000 -0.0003
## 480 0.0557 nan 0.1000 -0.0001
## 500 0.0506 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0349
## 2 1.1680 nan 0.1000 0.0354
## 3 1.1105 nan 0.1000 0.0243
## 4 1.0523 nan 0.1000 0.0215
## 5 1.0117 nan 0.1000 0.0157
## 6 0.9685 nan 0.1000 0.0194
## 7 0.9385 nan 0.1000 0.0125
## 8 0.9045 nan 0.1000 0.0151
## 9 0.8763 nan 0.1000 0.0117
## 10 0.8474 nan 0.1000 0.0116
## 20 0.6930 nan 0.1000 0.0045
## 40 0.5567 nan 0.1000 0.0004
## 60 0.4782 nan 0.1000 -0.0009
## 80 0.4172 nan 0.1000 -0.0014
## 100 0.3676 nan 0.1000 -0.0001
## 120 0.3307 nan 0.1000 -0.0001
## 140 0.2938 nan 0.1000 -0.0009
## 160 0.2598 nan 0.1000 0.0000
## 180 0.2334 nan 0.1000 -0.0009
## 200 0.2072 nan 0.1000 -0.0003
## 220 0.1873 nan 0.1000 -0.0013
## 240 0.1692 nan 0.1000 -0.0001
## 260 0.1542 nan 0.1000 -0.0001
## 280 0.1390 nan 0.1000 -0.0006
## 300 0.1258 nan 0.1000 -0.0001
## 320 0.1147 nan 0.1000 -0.0002
## 340 0.1043 nan 0.1000 -0.0003
## 360 0.0959 nan 0.1000 -0.0000
## 380 0.0873 nan 0.1000 -0.0004
## 400 0.0795 nan 0.1000 -0.0003
## 420 0.0733 nan 0.1000 -0.0002
## 440 0.0674 nan 0.1000 -0.0003
## 460 0.0619 nan 0.1000 -0.0002
## 480 0.0560 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2336 nan 0.1000 0.0349
## 2 1.1558 nan 0.1000 0.0339
## 3 1.0978 nan 0.1000 0.0240
## 4 1.0493 nan 0.1000 0.0195
## 5 1.0087 nan 0.1000 0.0154
## 6 0.9714 nan 0.1000 0.0175
## 7 0.9326 nan 0.1000 0.0157
## 8 0.9046 nan 0.1000 0.0123
## 9 0.8758 nan 0.1000 0.0113
## 10 0.8501 nan 0.1000 0.0104
## 20 0.7014 nan 0.1000 0.0038
## 40 0.5587 nan 0.1000 0.0008
## 60 0.4790 nan 0.1000 0.0004
## 80 0.4171 nan 0.1000 -0.0008
## 100 0.3684 nan 0.1000 -0.0001
## 120 0.3256 nan 0.1000 -0.0015
## 140 0.2939 nan 0.1000 -0.0008
## 160 0.2601 nan 0.1000 -0.0003
## 180 0.2312 nan 0.1000 -0.0003
## 200 0.2076 nan 0.1000 -0.0005
## 220 0.1886 nan 0.1000 -0.0004
## 240 0.1730 nan 0.1000 -0.0004
## 260 0.1569 nan 0.1000 -0.0008
## 280 0.1435 nan 0.1000 -0.0007
## 300 0.1303 nan 0.1000 -0.0006
## 320 0.1187 nan 0.1000 -0.0005
## 340 0.1093 nan 0.1000 -0.0003
## 360 0.1001 nan 0.1000 -0.0003
## 380 0.0922 nan 0.1000 -0.0002
## 400 0.0849 nan 0.1000 -0.0002
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## 460 0.0663 nan 0.1000 -0.0004
## 480 0.0614 nan 0.1000 -0.0001
## 500 0.0566 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2320 nan 0.1000 0.0441
## 2 1.1591 nan 0.1000 0.0300
## 3 1.0945 nan 0.1000 0.0265
## 4 1.0439 nan 0.1000 0.0209
## 5 0.9944 nan 0.1000 0.0233
## 6 0.9463 nan 0.1000 0.0190
## 7 0.9109 nan 0.1000 0.0123
## 8 0.8783 nan 0.1000 0.0113
## 9 0.8456 nan 0.1000 0.0118
## 10 0.8213 nan 0.1000 0.0096
## 20 0.6540 nan 0.1000 0.0022
## 40 0.4973 nan 0.1000 -0.0003
## 60 0.4093 nan 0.1000 -0.0005
## 80 0.3391 nan 0.1000 -0.0015
## 100 0.2866 nan 0.1000 -0.0008
## 120 0.2459 nan 0.1000 -0.0002
## 140 0.2135 nan 0.1000 -0.0005
## 160 0.1880 nan 0.1000 0.0002
## 180 0.1627 nan 0.1000 -0.0002
## 200 0.1402 nan 0.1000 -0.0003
## 220 0.1248 nan 0.1000 -0.0003
## 240 0.1107 nan 0.1000 -0.0002
## 260 0.0993 nan 0.1000 0.0000
## 280 0.0884 nan 0.1000 -0.0003
## 300 0.0795 nan 0.1000 -0.0005
## 320 0.0701 nan 0.1000 -0.0001
## 340 0.0621 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2313 nan 0.1000 0.0417
## 2 1.1568 nan 0.1000 0.0332
## 3 1.0923 nan 0.1000 0.0304
## 4 1.0340 nan 0.1000 0.0249
## 5 0.9843 nan 0.1000 0.0211
## 6 0.9444 nan 0.1000 0.0157
## 7 0.9097 nan 0.1000 0.0153
## 8 0.8808 nan 0.1000 0.0118
## 9 0.8523 nan 0.1000 0.0091
## 10 0.8277 nan 0.1000 0.0104
## 20 0.6610 nan 0.1000 0.0016
## 40 0.5180 nan 0.1000 0.0002
## 60 0.4212 nan 0.1000 0.0010
## 80 0.3612 nan 0.1000 -0.0008
## 100 0.3056 nan 0.1000 -0.0003
## 120 0.2656 nan 0.1000 -0.0010
## 140 0.2330 nan 0.1000 -0.0013
## 160 0.2003 nan 0.1000 -0.0001
## 180 0.1734 nan 0.1000 -0.0000
## 200 0.1547 nan 0.1000 -0.0004
## 220 0.1369 nan 0.1000 -0.0005
## 240 0.1217 nan 0.1000 -0.0002
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## 280 0.0985 nan 0.1000 -0.0004
## 300 0.0863 nan 0.1000 -0.0001
## 320 0.0769 nan 0.1000 -0.0005
## 340 0.0683 nan 0.1000 -0.0003
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## 380 0.0553 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0414
## 2 1.1618 nan 0.1000 0.0299
## 3 1.0965 nan 0.1000 0.0290
## 4 1.0419 nan 0.1000 0.0210
## 5 0.9944 nan 0.1000 0.0208
## 6 0.9507 nan 0.1000 0.0194
## 7 0.9141 nan 0.1000 0.0150
## 8 0.8766 nan 0.1000 0.0151
## 9 0.8505 nan 0.1000 0.0088
## 10 0.8218 nan 0.1000 0.0113
## 20 0.6629 nan 0.1000 0.0031
## 40 0.5239 nan 0.1000 0.0025
## 60 0.4436 nan 0.1000 -0.0020
## 80 0.3738 nan 0.1000 -0.0006
## 100 0.3177 nan 0.1000 -0.0018
## 120 0.2717 nan 0.1000 -0.0015
## 140 0.2354 nan 0.1000 -0.0003
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## 180 0.1881 nan 0.1000 -0.0007
## 200 0.1647 nan 0.1000 -0.0004
## 220 0.1432 nan 0.1000 -0.0008
## 240 0.1275 nan 0.1000 -0.0003
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## 280 0.1014 nan 0.1000 -0.0002
## 300 0.0896 nan 0.1000 -0.0001
## 320 0.0804 nan 0.1000 -0.0003
## 340 0.0719 nan 0.1000 -0.0002
## 360 0.0650 nan 0.1000 -0.0003
## 380 0.0589 nan 0.1000 -0.0004
## 400 0.0535 nan 0.1000 -0.0002
## 420 0.0478 nan 0.1000 -0.0001
## 440 0.0434 nan 0.1000 -0.0001
## 460 0.0391 nan 0.1000 -0.0000
## 480 0.0354 nan 0.1000 -0.0001
## 500 0.0322 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2213 nan 0.1000 0.0432
## 2 1.1463 nan 0.1000 0.0338
## 3 1.0837 nan 0.1000 0.0267
## 4 1.0260 nan 0.1000 0.0244
## 5 0.9761 nan 0.1000 0.0220
## 6 0.9334 nan 0.1000 0.0194
## 7 0.8910 nan 0.1000 0.0183
## 8 0.8577 nan 0.1000 0.0122
## 9 0.8298 nan 0.1000 0.0097
## 10 0.8001 nan 0.1000 0.0101
## 20 0.6191 nan 0.1000 0.0023
## 40 0.4543 nan 0.1000 -0.0010
## 60 0.3611 nan 0.1000 0.0008
## 80 0.2914 nan 0.1000 0.0000
## 100 0.2420 nan 0.1000 -0.0011
## 120 0.2048 nan 0.1000 -0.0009
## 140 0.1749 nan 0.1000 -0.0009
## 160 0.1509 nan 0.1000 -0.0001
## 180 0.1307 nan 0.1000 -0.0003
## 200 0.1113 nan 0.1000 -0.0001
## 220 0.0967 nan 0.1000 -0.0001
## 240 0.0839 nan 0.1000 -0.0002
## 260 0.0736 nan 0.1000 -0.0001
## 280 0.0648 nan 0.1000 -0.0002
## 300 0.0562 nan 0.1000 -0.0001
## 320 0.0498 nan 0.1000 -0.0001
## 340 0.0432 nan 0.1000 -0.0002
## 360 0.0375 nan 0.1000 -0.0001
## 380 0.0334 nan 0.1000 -0.0002
## 400 0.0294 nan 0.1000 -0.0001
## 420 0.0255 nan 0.1000 -0.0000
## 440 0.0225 nan 0.1000 -0.0000
## 460 0.0200 nan 0.1000 -0.0000
## 480 0.0176 nan 0.1000 -0.0000
## 500 0.0155 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2252 nan 0.1000 0.0409
## 2 1.1526 nan 0.1000 0.0307
## 3 1.0827 nan 0.1000 0.0349
## 4 1.0284 nan 0.1000 0.0260
## 5 0.9807 nan 0.1000 0.0174
## 6 0.9402 nan 0.1000 0.0180
## 7 0.9021 nan 0.1000 0.0154
## 8 0.8692 nan 0.1000 0.0139
## 9 0.8398 nan 0.1000 0.0121
## 10 0.8081 nan 0.1000 0.0132
## 20 0.6367 nan 0.1000 0.0015
## 40 0.4728 nan 0.1000 -0.0007
## 60 0.3807 nan 0.1000 0.0008
## 80 0.3123 nan 0.1000 -0.0015
## 100 0.2593 nan 0.1000 0.0004
## 120 0.2187 nan 0.1000 -0.0012
## 140 0.1828 nan 0.1000 -0.0002
## 160 0.1556 nan 0.1000 -0.0001
## 180 0.1335 nan 0.1000 -0.0007
## 200 0.1133 nan 0.1000 -0.0001
## 220 0.0977 nan 0.1000 -0.0003
## 240 0.0842 nan 0.1000 -0.0002
## 260 0.0729 nan 0.1000 -0.0001
## 280 0.0635 nan 0.1000 -0.0002
## 300 0.0551 nan 0.1000 -0.0003
## 320 0.0483 nan 0.1000 -0.0002
## 340 0.0417 nan 0.1000 -0.0002
## 360 0.0366 nan 0.1000 -0.0000
## 380 0.0322 nan 0.1000 -0.0000
## 400 0.0279 nan 0.1000 -0.0001
## 420 0.0244 nan 0.1000 -0.0000
## 440 0.0213 nan 0.1000 -0.0001
## 460 0.0186 nan 0.1000 -0.0001
## 480 0.0162 nan 0.1000 -0.0000
## 500 0.0141 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2274 nan 0.1000 0.0426
## 2 1.1597 nan 0.1000 0.0303
## 3 1.0938 nan 0.1000 0.0281
## 4 1.0353 nan 0.1000 0.0258
## 5 0.9823 nan 0.1000 0.0193
## 6 0.9430 nan 0.1000 0.0175
## 7 0.9051 nan 0.1000 0.0159
## 8 0.8715 nan 0.1000 0.0141
## 9 0.8385 nan 0.1000 0.0122
## 10 0.8134 nan 0.1000 0.0096
## 20 0.6417 nan 0.1000 0.0017
## 40 0.4875 nan 0.1000 -0.0005
## 60 0.3988 nan 0.1000 -0.0018
## 80 0.3284 nan 0.1000 -0.0001
## 100 0.2746 nan 0.1000 -0.0002
## 120 0.2292 nan 0.1000 -0.0008
## 140 0.1931 nan 0.1000 -0.0002
## 160 0.1646 nan 0.1000 -0.0005
## 180 0.1428 nan 0.1000 -0.0004
## 200 0.1225 nan 0.1000 -0.0002
## 220 0.1072 nan 0.1000 -0.0007
## 240 0.0935 nan 0.1000 -0.0003
## 260 0.0814 nan 0.1000 -0.0002
## 280 0.0708 nan 0.1000 -0.0003
## 300 0.0632 nan 0.1000 -0.0001
## 320 0.0556 nan 0.1000 -0.0003
## 340 0.0490 nan 0.1000 0.0000
## 360 0.0434 nan 0.1000 -0.0001
## 380 0.0381 nan 0.1000 -0.0001
## 400 0.0334 nan 0.1000 -0.0001
## 420 0.0293 nan 0.1000 -0.0000
## 440 0.0259 nan 0.1000 -0.0000
## 460 0.0224 nan 0.1000 -0.0000
## 480 0.0199 nan 0.1000 -0.0000
## 500 0.0175 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0003
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0003
## 5 1.3166 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3150 nan 0.0010 0.0004
## 8 1.3142 nan 0.0010 0.0003
## 9 1.3134 nan 0.0010 0.0003
## 10 1.3126 nan 0.0010 0.0004
## 20 1.3046 nan 0.0010 0.0003
## 40 1.2889 nan 0.0010 0.0003
## 60 1.2735 nan 0.0010 0.0003
## 80 1.2586 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2038 nan 0.0010 0.0003
## 180 1.1910 nan 0.0010 0.0003
## 200 1.1786 nan 0.0010 0.0002
## 220 1.1665 nan 0.0010 0.0002
## 240 1.1549 nan 0.0010 0.0002
## 260 1.1439 nan 0.0010 0.0002
## 280 1.1329 nan 0.0010 0.0002
## 300 1.1222 nan 0.0010 0.0002
## 320 1.1120 nan 0.0010 0.0002
## 340 1.1019 nan 0.0010 0.0002
## 360 1.0920 nan 0.0010 0.0002
## 380 1.0826 nan 0.0010 0.0002
## 400 1.0734 nan 0.0010 0.0002
## 420 1.0643 nan 0.0010 0.0002
## 440 1.0556 nan 0.0010 0.0002
## 460 1.0470 nan 0.0010 0.0002
## 480 1.0387 nan 0.0010 0.0002
## 500 1.0304 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3181 nan 0.0010 0.0004
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3156 nan 0.0010 0.0005
## 7 1.3148 nan 0.0010 0.0003
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3040 nan 0.0010 0.0004
## 40 1.2886 nan 0.0010 0.0003
## 60 1.2734 nan 0.0010 0.0003
## 80 1.2588 nan 0.0010 0.0003
## 100 1.2443 nan 0.0010 0.0003
## 120 1.2305 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2040 nan 0.0010 0.0003
## 180 1.1914 nan 0.0010 0.0002
## 200 1.1789 nan 0.0010 0.0003
## 220 1.1670 nan 0.0010 0.0003
## 240 1.1554 nan 0.0010 0.0003
## 260 1.1442 nan 0.0010 0.0003
## 280 1.1335 nan 0.0010 0.0002
## 300 1.1230 nan 0.0010 0.0002
## 320 1.1127 nan 0.0010 0.0002
## 340 1.1024 nan 0.0010 0.0002
## 360 1.0926 nan 0.0010 0.0002
## 380 1.0831 nan 0.0010 0.0002
## 400 1.0739 nan 0.0010 0.0002
## 420 1.0650 nan 0.0010 0.0002
## 440 1.0564 nan 0.0010 0.0002
## 460 1.0478 nan 0.0010 0.0001
## 480 1.0393 nan 0.0010 0.0002
## 500 1.0312 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3199 nan 0.0010 0.0004
## 2 1.3190 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3165 nan 0.0010 0.0004
## 6 1.3157 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3042 nan 0.0010 0.0003
## 40 1.2884 nan 0.0010 0.0003
## 60 1.2729 nan 0.0010 0.0004
## 80 1.2582 nan 0.0010 0.0003
## 100 1.2441 nan 0.0010 0.0002
## 120 1.2304 nan 0.0010 0.0003
## 140 1.2171 nan 0.0010 0.0003
## 160 1.2042 nan 0.0010 0.0003
## 180 1.1915 nan 0.0010 0.0003
## 200 1.1794 nan 0.0010 0.0002
## 220 1.1676 nan 0.0010 0.0003
## 240 1.1561 nan 0.0010 0.0002
## 260 1.1451 nan 0.0010 0.0002
## 280 1.1342 nan 0.0010 0.0003
## 300 1.1237 nan 0.0010 0.0002
## 320 1.1135 nan 0.0010 0.0002
## 340 1.1035 nan 0.0010 0.0002
## 360 1.0938 nan 0.0010 0.0002
## 380 1.0844 nan 0.0010 0.0002
## 400 1.0750 nan 0.0010 0.0002
## 420 1.0660 nan 0.0010 0.0002
## 440 1.0573 nan 0.0010 0.0002
## 460 1.0487 nan 0.0010 0.0002
## 480 1.0405 nan 0.0010 0.0002
## 500 1.0324 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0005
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3143 nan 0.0010 0.0004
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0004
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3028 nan 0.0010 0.0004
## 40 1.2858 nan 0.0010 0.0004
## 60 1.2692 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0003
## 100 1.2382 nan 0.0010 0.0003
## 120 1.2232 nan 0.0010 0.0004
## 140 1.2091 nan 0.0010 0.0003
## 160 1.1950 nan 0.0010 0.0003
## 180 1.1816 nan 0.0010 0.0003
## 200 1.1683 nan 0.0010 0.0003
## 220 1.1554 nan 0.0010 0.0003
## 240 1.1428 nan 0.0010 0.0003
## 260 1.1309 nan 0.0010 0.0003
## 280 1.1194 nan 0.0010 0.0003
## 300 1.1079 nan 0.0010 0.0002
## 320 1.0970 nan 0.0010 0.0002
## 340 1.0862 nan 0.0010 0.0002
## 360 1.0757 nan 0.0010 0.0002
## 380 1.0656 nan 0.0010 0.0002
## 400 1.0557 nan 0.0010 0.0002
## 420 1.0461 nan 0.0010 0.0002
## 440 1.0368 nan 0.0010 0.0001
## 460 1.0277 nan 0.0010 0.0001
## 480 1.0189 nan 0.0010 0.0002
## 500 1.0104 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3188 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0003
## 5 1.3162 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3030 nan 0.0010 0.0004
## 40 1.2860 nan 0.0010 0.0003
## 60 1.2699 nan 0.0010 0.0004
## 80 1.2538 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2240 nan 0.0010 0.0004
## 140 1.2097 nan 0.0010 0.0003
## 160 1.1957 nan 0.0010 0.0003
## 180 1.1824 nan 0.0010 0.0003
## 200 1.1695 nan 0.0010 0.0003
## 220 1.1570 nan 0.0010 0.0002
## 240 1.1449 nan 0.0010 0.0003
## 260 1.1329 nan 0.0010 0.0003
## 280 1.1215 nan 0.0010 0.0002
## 300 1.1105 nan 0.0010 0.0003
## 320 1.0995 nan 0.0010 0.0002
## 340 1.0888 nan 0.0010 0.0002
## 360 1.0785 nan 0.0010 0.0002
## 380 1.0686 nan 0.0010 0.0002
## 400 1.0590 nan 0.0010 0.0002
## 420 1.0496 nan 0.0010 0.0001
## 440 1.0401 nan 0.0010 0.0002
## 460 1.0311 nan 0.0010 0.0002
## 480 1.0224 nan 0.0010 0.0002
## 500 1.0138 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3198 nan 0.0010 0.0004
## 2 1.3189 nan 0.0010 0.0004
## 3 1.3180 nan 0.0010 0.0004
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0004
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3136 nan 0.0010 0.0004
## 9 1.3127 nan 0.0010 0.0004
## 10 1.3119 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2861 nan 0.0010 0.0003
## 60 1.2702 nan 0.0010 0.0003
## 80 1.2541 nan 0.0010 0.0004
## 100 1.2390 nan 0.0010 0.0004
## 120 1.2243 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1970 nan 0.0010 0.0003
## 180 1.1840 nan 0.0010 0.0003
## 200 1.1712 nan 0.0010 0.0003
## 220 1.1588 nan 0.0010 0.0003
## 240 1.1466 nan 0.0010 0.0003
## 260 1.1348 nan 0.0010 0.0003
## 280 1.1233 nan 0.0010 0.0002
## 300 1.1123 nan 0.0010 0.0002
## 320 1.1015 nan 0.0010 0.0002
## 340 1.0910 nan 0.0010 0.0002
## 360 1.0811 nan 0.0010 0.0002
## 380 1.0711 nan 0.0010 0.0002
## 400 1.0613 nan 0.0010 0.0002
## 420 1.0518 nan 0.0010 0.0002
## 440 1.0426 nan 0.0010 0.0002
## 460 1.0334 nan 0.0010 0.0002
## 480 1.0249 nan 0.0010 0.0002
## 500 1.0163 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0005
## 4 1.3167 nan 0.0010 0.0004
## 5 1.3158 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2840 nan 0.0010 0.0004
## 60 1.2667 nan 0.0010 0.0004
## 80 1.2501 nan 0.0010 0.0004
## 100 1.2340 nan 0.0010 0.0003
## 120 1.2187 nan 0.0010 0.0004
## 140 1.2038 nan 0.0010 0.0004
## 160 1.1889 nan 0.0010 0.0003
## 180 1.1745 nan 0.0010 0.0003
## 200 1.1609 nan 0.0010 0.0003
## 220 1.1477 nan 0.0010 0.0003
## 240 1.1348 nan 0.0010 0.0003
## 260 1.1222 nan 0.0010 0.0003
## 280 1.1098 nan 0.0010 0.0003
## 300 1.0981 nan 0.0010 0.0003
## 320 1.0868 nan 0.0010 0.0002
## 340 1.0758 nan 0.0010 0.0002
## 360 1.0651 nan 0.0010 0.0002
## 380 1.0546 nan 0.0010 0.0002
## 400 1.0442 nan 0.0010 0.0002
## 420 1.0341 nan 0.0010 0.0002
## 440 1.0245 nan 0.0010 0.0002
## 460 1.0149 nan 0.0010 0.0002
## 480 1.0058 nan 0.0010 0.0002
## 500 0.9970 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3178 nan 0.0010 0.0004
## 4 1.3169 nan 0.0010 0.0004
## 5 1.3160 nan 0.0010 0.0004
## 6 1.3150 nan 0.0010 0.0005
## 7 1.3140 nan 0.0010 0.0004
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2839 nan 0.0010 0.0004
## 60 1.2669 nan 0.0010 0.0003
## 80 1.2503 nan 0.0010 0.0004
## 100 1.2343 nan 0.0010 0.0004
## 120 1.2189 nan 0.0010 0.0003
## 140 1.2037 nan 0.0010 0.0003
## 160 1.1891 nan 0.0010 0.0003
## 180 1.1752 nan 0.0010 0.0003
## 200 1.1618 nan 0.0010 0.0003
## 220 1.1486 nan 0.0010 0.0003
## 240 1.1355 nan 0.0010 0.0003
## 260 1.1231 nan 0.0010 0.0003
## 280 1.1112 nan 0.0010 0.0002
## 300 1.0994 nan 0.0010 0.0003
## 320 1.0882 nan 0.0010 0.0002
## 340 1.0771 nan 0.0010 0.0002
## 360 1.0663 nan 0.0010 0.0002
## 380 1.0558 nan 0.0010 0.0002
## 400 1.0454 nan 0.0010 0.0002
## 420 1.0353 nan 0.0010 0.0002
## 440 1.0256 nan 0.0010 0.0002
## 460 1.0166 nan 0.0010 0.0002
## 480 1.0076 nan 0.0010 0.0002
## 500 0.9987 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3197 nan 0.0010 0.0004
## 2 1.3187 nan 0.0010 0.0004
## 3 1.3177 nan 0.0010 0.0004
## 4 1.3168 nan 0.0010 0.0004
## 5 1.3159 nan 0.0010 0.0004
## 6 1.3149 nan 0.0010 0.0004
## 7 1.3139 nan 0.0010 0.0005
## 8 1.3131 nan 0.0010 0.0004
## 9 1.3121 nan 0.0010 0.0004
## 10 1.3113 nan 0.0010 0.0004
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2854 nan 0.0010 0.0004
## 60 1.2683 nan 0.0010 0.0004
## 80 1.2525 nan 0.0010 0.0003
## 100 1.2368 nan 0.0010 0.0004
## 120 1.2216 nan 0.0010 0.0003
## 140 1.2069 nan 0.0010 0.0003
## 160 1.1923 nan 0.0010 0.0003
## 180 1.1783 nan 0.0010 0.0003
## 200 1.1647 nan 0.0010 0.0002
## 220 1.1515 nan 0.0010 0.0003
## 240 1.1389 nan 0.0010 0.0003
## 260 1.1265 nan 0.0010 0.0003
## 280 1.1145 nan 0.0010 0.0003
## 300 1.1029 nan 0.0010 0.0003
## 320 1.0917 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0002
## 360 1.0701 nan 0.0010 0.0002
## 380 1.0596 nan 0.0010 0.0002
## 400 1.0494 nan 0.0010 0.0002
## 420 1.0399 nan 0.0010 0.0002
## 440 1.0305 nan 0.0010 0.0002
## 460 1.0214 nan 0.0010 0.0002
## 480 1.0124 nan 0.0010 0.0002
## 500 1.0037 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3129 nan 0.0100 0.0035
## 2 1.3037 nan 0.0100 0.0042
## 3 1.2967 nan 0.0100 0.0031
## 4 1.2886 nan 0.0100 0.0037
## 5 1.2806 nan 0.0100 0.0034
## 6 1.2728 nan 0.0100 0.0035
## 7 1.2650 nan 0.0100 0.0033
## 8 1.2580 nan 0.0100 0.0029
## 9 1.2508 nan 0.0100 0.0032
## 10 1.2432 nan 0.0100 0.0036
## 20 1.1779 nan 0.0100 0.0029
## 40 1.0731 nan 0.0100 0.0020
## 60 0.9909 nan 0.0100 0.0016
## 80 0.9278 nan 0.0100 0.0012
## 100 0.8764 nan 0.0100 0.0008
## 120 0.8344 nan 0.0100 0.0004
## 140 0.7997 nan 0.0100 0.0004
## 160 0.7703 nan 0.0100 0.0000
## 180 0.7457 nan 0.0100 0.0003
## 200 0.7248 nan 0.0100 0.0002
## 220 0.7062 nan 0.0100 0.0000
## 240 0.6885 nan 0.0100 0.0001
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## 280 0.6576 nan 0.0100 0.0001
## 300 0.6441 nan 0.0100 0.0001
## 320 0.6322 nan 0.0100 0.0000
## 340 0.6197 nan 0.0100 0.0001
## 360 0.6094 nan 0.0100 0.0000
## 380 0.5983 nan 0.0100 0.0000
## 400 0.5890 nan 0.0100 -0.0000
## 420 0.5796 nan 0.0100 -0.0001
## 440 0.5706 nan 0.0100 -0.0001
## 460 0.5618 nan 0.0100 -0.0001
## 480 0.5531 nan 0.0100 -0.0002
## 500 0.5451 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3041 nan 0.0100 0.0036
## 3 1.2960 nan 0.0100 0.0035
## 4 1.2879 nan 0.0100 0.0040
## 5 1.2799 nan 0.0100 0.0034
## 6 1.2729 nan 0.0100 0.0029
## 7 1.2653 nan 0.0100 0.0037
## 8 1.2576 nan 0.0100 0.0032
## 9 1.2501 nan 0.0100 0.0034
## 10 1.2428 nan 0.0100 0.0029
## 20 1.1771 nan 0.0100 0.0022
## 40 1.0737 nan 0.0100 0.0021
## 60 0.9929 nan 0.0100 0.0017
## 80 0.9297 nan 0.0100 0.0012
## 100 0.8790 nan 0.0100 0.0008
## 120 0.8368 nan 0.0100 0.0005
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## 160 0.7749 nan 0.0100 0.0004
## 180 0.7491 nan 0.0100 0.0003
## 200 0.7280 nan 0.0100 0.0001
## 220 0.7097 nan 0.0100 -0.0000
## 240 0.6936 nan 0.0100 -0.0000
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## 280 0.6657 nan 0.0100 0.0000
## 300 0.6530 nan 0.0100 0.0002
## 320 0.6405 nan 0.0100 0.0001
## 340 0.6298 nan 0.0100 -0.0001
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## 380 0.6095 nan 0.0100 -0.0000
## 400 0.5999 nan 0.0100 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3124 nan 0.0100 0.0039
## 2 1.3047 nan 0.0100 0.0035
## 3 1.2967 nan 0.0100 0.0037
## 4 1.2885 nan 0.0100 0.0038
## 5 1.2806 nan 0.0100 0.0039
## 6 1.2743 nan 0.0100 0.0031
## 7 1.2668 nan 0.0100 0.0030
## 8 1.2599 nan 0.0100 0.0032
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## 10 1.2454 nan 0.0100 0.0031
## 20 1.1801 nan 0.0100 0.0029
## 40 1.0771 nan 0.0100 0.0021
## 60 0.9967 nan 0.0100 0.0015
## 80 0.9323 nan 0.0100 0.0011
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## 180 0.7481 nan 0.0100 0.0003
## 200 0.7272 nan 0.0100 0.0002
## 220 0.7086 nan 0.0100 0.0001
## 240 0.6933 nan 0.0100 0.0001
## 260 0.6787 nan 0.0100 0.0000
## 280 0.6655 nan 0.0100 -0.0001
## 300 0.6541 nan 0.0100 0.0002
## 320 0.6415 nan 0.0100 0.0001
## 340 0.6311 nan 0.0100 0.0000
## 360 0.6213 nan 0.0100 0.0001
## 380 0.6105 nan 0.0100 -0.0001
## 400 0.6016 nan 0.0100 0.0000
## 420 0.5915 nan 0.0100 -0.0000
## 440 0.5824 nan 0.0100 -0.0001
## 460 0.5738 nan 0.0100 -0.0000
## 480 0.5664 nan 0.0100 -0.0000
## 500 0.5579 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0040
## 2 1.3034 nan 0.0100 0.0041
## 3 1.2949 nan 0.0100 0.0040
## 4 1.2863 nan 0.0100 0.0039
## 5 1.2780 nan 0.0100 0.0037
## 6 1.2702 nan 0.0100 0.0036
## 7 1.2627 nan 0.0100 0.0034
## 8 1.2549 nan 0.0100 0.0038
## 9 1.2473 nan 0.0100 0.0033
## 10 1.2396 nan 0.0100 0.0038
## 20 1.1698 nan 0.0100 0.0033
## 40 1.0584 nan 0.0100 0.0020
## 60 0.9733 nan 0.0100 0.0016
## 80 0.9061 nan 0.0100 0.0012
## 100 0.8521 nan 0.0100 0.0011
## 120 0.8075 nan 0.0100 0.0006
## 140 0.7708 nan 0.0100 0.0006
## 160 0.7394 nan 0.0100 0.0004
## 180 0.7132 nan 0.0100 0.0004
## 200 0.6910 nan 0.0100 0.0003
## 220 0.6700 nan 0.0100 0.0001
## 240 0.6509 nan 0.0100 0.0001
## 260 0.6335 nan 0.0100 0.0002
## 280 0.6182 nan 0.0100 0.0002
## 300 0.6026 nan 0.0100 0.0000
## 320 0.5886 nan 0.0100 -0.0001
## 340 0.5763 nan 0.0100 0.0000
## 360 0.5641 nan 0.0100 -0.0002
## 380 0.5536 nan 0.0100 -0.0001
## 400 0.5428 nan 0.0100 -0.0002
## 420 0.5320 nan 0.0100 0.0001
## 440 0.5211 nan 0.0100 0.0002
## 460 0.5100 nan 0.0100 -0.0001
## 480 0.5004 nan 0.0100 -0.0001
## 500 0.4924 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0038
## 2 1.3028 nan 0.0100 0.0041
## 3 1.2937 nan 0.0100 0.0040
## 4 1.2854 nan 0.0100 0.0037
## 5 1.2771 nan 0.0100 0.0032
## 6 1.2695 nan 0.0100 0.0035
## 7 1.2614 nan 0.0100 0.0036
## 8 1.2531 nan 0.0100 0.0038
## 9 1.2456 nan 0.0100 0.0037
## 10 1.2384 nan 0.0100 0.0028
## 20 1.1713 nan 0.0100 0.0028
## 40 1.0581 nan 0.0100 0.0022
## 60 0.9730 nan 0.0100 0.0015
## 80 0.9036 nan 0.0100 0.0011
## 100 0.8504 nan 0.0100 0.0007
## 120 0.8079 nan 0.0100 0.0006
## 140 0.7723 nan 0.0100 0.0005
## 160 0.7412 nan 0.0100 0.0005
## 180 0.7155 nan 0.0100 0.0005
## 200 0.6922 nan 0.0100 0.0003
## 220 0.6707 nan 0.0100 0.0001
## 240 0.6535 nan 0.0100 0.0002
## 260 0.6368 nan 0.0100 0.0001
## 280 0.6216 nan 0.0100 0.0002
## 300 0.6074 nan 0.0100 -0.0000
## 320 0.5943 nan 0.0100 -0.0002
## 340 0.5820 nan 0.0100 0.0001
## 360 0.5700 nan 0.0100 -0.0000
## 380 0.5595 nan 0.0100 -0.0001
## 400 0.5479 nan 0.0100 0.0000
## 420 0.5368 nan 0.0100 -0.0001
## 440 0.5264 nan 0.0100 -0.0001
## 460 0.5168 nan 0.0100 -0.0000
## 480 0.5068 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3121 nan 0.0100 0.0035
## 2 1.3028 nan 0.0100 0.0042
## 3 1.2940 nan 0.0100 0.0042
## 4 1.2856 nan 0.0100 0.0038
## 5 1.2783 nan 0.0100 0.0031
## 6 1.2706 nan 0.0100 0.0035
## 7 1.2627 nan 0.0100 0.0036
## 8 1.2556 nan 0.0100 0.0034
## 9 1.2480 nan 0.0100 0.0033
## 10 1.2408 nan 0.0100 0.0026
## 20 1.1700 nan 0.0100 0.0032
## 40 1.0617 nan 0.0100 0.0021
## 60 0.9792 nan 0.0100 0.0011
## 80 0.9120 nan 0.0100 0.0011
## 100 0.8599 nan 0.0100 0.0008
## 120 0.8163 nan 0.0100 0.0009
## 140 0.7817 nan 0.0100 0.0004
## 160 0.7523 nan 0.0100 0.0003
## 180 0.7241 nan 0.0100 0.0005
## 200 0.7017 nan 0.0100 0.0002
## 220 0.6810 nan 0.0100 0.0001
## 240 0.6617 nan 0.0100 0.0002
## 260 0.6461 nan 0.0100 -0.0001
## 280 0.6321 nan 0.0100 0.0001
## 300 0.6178 nan 0.0100 0.0002
## 320 0.6062 nan 0.0100 0.0000
## 340 0.5932 nan 0.0100 0.0001
## 360 0.5807 nan 0.0100 0.0001
## 380 0.5697 nan 0.0100 -0.0001
## 400 0.5585 nan 0.0100 -0.0002
## 420 0.5473 nan 0.0100 -0.0001
## 440 0.5376 nan 0.0100 0.0000
## 460 0.5281 nan 0.0100 -0.0000
## 480 0.5189 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3112 nan 0.0100 0.0043
## 2 1.3025 nan 0.0100 0.0039
## 3 1.2939 nan 0.0100 0.0040
## 4 1.2845 nan 0.0100 0.0041
## 5 1.2754 nan 0.0100 0.0041
## 6 1.2668 nan 0.0100 0.0038
## 7 1.2586 nan 0.0100 0.0031
## 8 1.2493 nan 0.0100 0.0042
## 9 1.2416 nan 0.0100 0.0036
## 10 1.2341 nan 0.0100 0.0036
## 20 1.1629 nan 0.0100 0.0029
## 40 1.0458 nan 0.0100 0.0020
## 60 0.9554 nan 0.0100 0.0014
## 80 0.8841 nan 0.0100 0.0011
## 100 0.8301 nan 0.0100 0.0008
## 120 0.7839 nan 0.0100 0.0006
## 140 0.7460 nan 0.0100 0.0004
## 160 0.7117 nan 0.0100 0.0005
## 180 0.6832 nan 0.0100 0.0003
## 200 0.6586 nan 0.0100 0.0004
## 220 0.6358 nan 0.0100 0.0001
## 240 0.6163 nan 0.0100 0.0003
## 260 0.5979 nan 0.0100 -0.0001
## 280 0.5813 nan 0.0100 -0.0000
## 300 0.5658 nan 0.0100 0.0001
## 320 0.5501 nan 0.0100 0.0001
## 340 0.5372 nan 0.0100 -0.0001
## 360 0.5238 nan 0.0100 0.0002
## 380 0.5116 nan 0.0100 -0.0001
## 400 0.4989 nan 0.0100 0.0000
## 420 0.4865 nan 0.0100 0.0000
## 440 0.4762 nan 0.0100 -0.0001
## 460 0.4659 nan 0.0100 -0.0000
## 480 0.4570 nan 0.0100 -0.0000
## 500 0.4472 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3113 nan 0.0100 0.0043
## 2 1.3018 nan 0.0100 0.0042
## 3 1.2935 nan 0.0100 0.0037
## 4 1.2844 nan 0.0100 0.0040
## 5 1.2753 nan 0.0100 0.0041
## 6 1.2667 nan 0.0100 0.0037
## 7 1.2588 nan 0.0100 0.0041
## 8 1.2510 nan 0.0100 0.0037
## 9 1.2423 nan 0.0100 0.0040
## 10 1.2339 nan 0.0100 0.0039
## 20 1.1605 nan 0.0100 0.0031
## 40 1.0445 nan 0.0100 0.0021
## 60 0.9549 nan 0.0100 0.0015
## 80 0.8857 nan 0.0100 0.0014
## 100 0.8286 nan 0.0100 0.0008
## 120 0.7838 nan 0.0100 0.0006
## 140 0.7463 nan 0.0100 0.0005
## 160 0.7140 nan 0.0100 0.0003
## 180 0.6853 nan 0.0100 0.0003
## 200 0.6618 nan 0.0100 0.0002
## 220 0.6400 nan 0.0100 0.0003
## 240 0.6200 nan 0.0100 0.0001
## 260 0.6029 nan 0.0100 0.0001
## 280 0.5868 nan 0.0100 -0.0001
## 300 0.5719 nan 0.0100 0.0001
## 320 0.5577 nan 0.0100 0.0001
## 340 0.5454 nan 0.0100 0.0000
## 360 0.5324 nan 0.0100 -0.0000
## 380 0.5198 nan 0.0100 0.0001
## 400 0.5078 nan 0.0100 -0.0000
## 420 0.4967 nan 0.0100 0.0001
## 440 0.4857 nan 0.0100 -0.0000
## 460 0.4770 nan 0.0100 -0.0000
## 480 0.4675 nan 0.0100 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3116 nan 0.0100 0.0045
## 2 1.3026 nan 0.0100 0.0038
## 3 1.2945 nan 0.0100 0.0038
## 4 1.2854 nan 0.0100 0.0041
## 5 1.2769 nan 0.0100 0.0038
## 6 1.2684 nan 0.0100 0.0038
## 7 1.2604 nan 0.0100 0.0035
## 8 1.2530 nan 0.0100 0.0034
## 9 1.2448 nan 0.0100 0.0038
## 10 1.2363 nan 0.0100 0.0039
## 20 1.1636 nan 0.0100 0.0032
## 40 1.0503 nan 0.0100 0.0019
## 60 0.9626 nan 0.0100 0.0010
## 80 0.8935 nan 0.0100 0.0014
## 100 0.8392 nan 0.0100 0.0009
## 120 0.7943 nan 0.0100 0.0006
## 140 0.7574 nan 0.0100 0.0004
## 160 0.7272 nan 0.0100 0.0005
## 180 0.6987 nan 0.0100 0.0003
## 200 0.6747 nan 0.0100 0.0001
## 220 0.6532 nan 0.0100 -0.0000
## 240 0.6342 nan 0.0100 -0.0001
## 260 0.6166 nan 0.0100 0.0000
## 280 0.6002 nan 0.0100 0.0000
## 300 0.5845 nan 0.0100 0.0001
## 320 0.5715 nan 0.0100 0.0002
## 340 0.5584 nan 0.0100 -0.0002
## 360 0.5458 nan 0.0100 -0.0001
## 380 0.5340 nan 0.0100 0.0000
## 400 0.5230 nan 0.0100 0.0000
## 420 0.5122 nan 0.0100 0.0001
## 440 0.5002 nan 0.0100 0.0001
## 460 0.4900 nan 0.0100 0.0001
## 480 0.4805 nan 0.0100 -0.0000
## 500 0.4706 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2345 nan 0.1000 0.0401
## 2 1.1729 nan 0.1000 0.0282
## 3 1.1151 nan 0.1000 0.0269
## 4 1.0683 nan 0.1000 0.0214
## 5 1.0295 nan 0.1000 0.0194
## 6 0.9887 nan 0.1000 0.0147
## 7 0.9573 nan 0.1000 0.0123
## 8 0.9280 nan 0.1000 0.0115
## 9 0.9019 nan 0.1000 0.0074
## 10 0.8770 nan 0.1000 0.0098
## 20 0.7268 nan 0.1000 0.0026
## 40 0.5977 nan 0.1000 -0.0007
## 60 0.5136 nan 0.1000 -0.0005
## 80 0.4465 nan 0.1000 -0.0005
## 100 0.3950 nan 0.1000 0.0009
## 120 0.3512 nan 0.1000 -0.0012
## 140 0.3122 nan 0.1000 -0.0004
## 160 0.2791 nan 0.1000 0.0001
## 180 0.2496 nan 0.1000 -0.0004
## 200 0.2267 nan 0.1000 -0.0009
## 220 0.2059 nan 0.1000 -0.0007
## 240 0.1874 nan 0.1000 -0.0008
## 260 0.1736 nan 0.1000 -0.0002
## 280 0.1619 nan 0.1000 -0.0005
## 300 0.1493 nan 0.1000 -0.0002
## 320 0.1372 nan 0.1000 -0.0003
## 340 0.1251 nan 0.1000 0.0001
## 360 0.1153 nan 0.1000 -0.0004
## 380 0.1057 nan 0.1000 -0.0001
## 400 0.0967 nan 0.1000 -0.0001
## 420 0.0893 nan 0.1000 -0.0003
## 440 0.0833 nan 0.1000 -0.0002
## 460 0.0757 nan 0.1000 0.0001
## 480 0.0702 nan 0.1000 -0.0001
## 500 0.0656 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0396
## 2 1.1715 nan 0.1000 0.0296
## 3 1.1201 nan 0.1000 0.0225
## 4 1.0728 nan 0.1000 0.0202
## 5 1.0336 nan 0.1000 0.0167
## 6 0.9942 nan 0.1000 0.0170
## 7 0.9602 nan 0.1000 0.0107
## 8 0.9294 nan 0.1000 0.0107
## 9 0.8992 nan 0.1000 0.0116
## 10 0.8719 nan 0.1000 0.0101
## 20 0.7256 nan 0.1000 0.0024
## 40 0.5916 nan 0.1000 0.0000
## 60 0.5171 nan 0.1000 -0.0012
## 80 0.4477 nan 0.1000 -0.0003
## 100 0.3955 nan 0.1000 -0.0013
## 120 0.3519 nan 0.1000 -0.0008
## 140 0.3196 nan 0.1000 -0.0000
## 160 0.2869 nan 0.1000 -0.0001
## 180 0.2604 nan 0.1000 -0.0004
## 200 0.2367 nan 0.1000 -0.0010
## 220 0.2190 nan 0.1000 -0.0006
## 240 0.1977 nan 0.1000 -0.0013
## 260 0.1806 nan 0.1000 -0.0003
## 280 0.1631 nan 0.1000 -0.0003
## 300 0.1504 nan 0.1000 -0.0004
## 320 0.1372 nan 0.1000 -0.0000
## 340 0.1255 nan 0.1000 -0.0003
## 360 0.1170 nan 0.1000 -0.0008
## 380 0.1085 nan 0.1000 -0.0004
## 400 0.0994 nan 0.1000 -0.0001
## 420 0.0910 nan 0.1000 -0.0004
## 440 0.0845 nan 0.1000 -0.0003
## 460 0.0785 nan 0.1000 -0.0000
## 480 0.0724 nan 0.1000 -0.0001
## 500 0.0666 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2431 nan 0.1000 0.0378
## 2 1.1782 nan 0.1000 0.0298
## 3 1.1216 nan 0.1000 0.0261
## 4 1.0745 nan 0.1000 0.0189
## 5 1.0298 nan 0.1000 0.0191
## 6 0.9929 nan 0.1000 0.0146
## 7 0.9631 nan 0.1000 0.0121
## 8 0.9307 nan 0.1000 0.0123
## 9 0.9023 nan 0.1000 0.0122
## 10 0.8818 nan 0.1000 0.0071
## 20 0.7278 nan 0.1000 0.0026
## 40 0.6070 nan 0.1000 -0.0006
## 60 0.5341 nan 0.1000 0.0005
## 80 0.4740 nan 0.1000 -0.0007
## 100 0.4226 nan 0.1000 -0.0003
## 120 0.3808 nan 0.1000 -0.0011
## 140 0.3447 nan 0.1000 -0.0006
## 160 0.3136 nan 0.1000 -0.0015
## 180 0.2837 nan 0.1000 -0.0013
## 200 0.2569 nan 0.1000 -0.0008
## 220 0.2336 nan 0.1000 -0.0002
## 240 0.2160 nan 0.1000 -0.0006
## 260 0.1945 nan 0.1000 -0.0001
## 280 0.1773 nan 0.1000 -0.0007
## 300 0.1629 nan 0.1000 -0.0007
## 320 0.1499 nan 0.1000 -0.0004
## 340 0.1378 nan 0.1000 -0.0002
## 360 0.1263 nan 0.1000 -0.0004
## 380 0.1164 nan 0.1000 -0.0002
## 400 0.1077 nan 0.1000 -0.0001
## 420 0.0987 nan 0.1000 -0.0002
## 440 0.0919 nan 0.1000 -0.0001
## 460 0.0857 nan 0.1000 -0.0004
## 480 0.0791 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0384
## 2 1.1678 nan 0.1000 0.0297
## 3 1.1107 nan 0.1000 0.0258
## 4 1.0557 nan 0.1000 0.0220
## 5 1.0125 nan 0.1000 0.0161
## 6 0.9763 nan 0.1000 0.0147
## 7 0.9401 nan 0.1000 0.0152
## 8 0.9068 nan 0.1000 0.0130
## 9 0.8778 nan 0.1000 0.0101
## 10 0.8511 nan 0.1000 0.0116
## 20 0.6976 nan 0.1000 0.0019
## 40 0.5496 nan 0.1000 0.0004
## 60 0.4642 nan 0.1000 -0.0009
## 80 0.3916 nan 0.1000 -0.0016
## 100 0.3333 nan 0.1000 -0.0016
## 120 0.2878 nan 0.1000 0.0000
## 140 0.2527 nan 0.1000 -0.0013
## 160 0.2210 nan 0.1000 0.0000
## 180 0.1970 nan 0.1000 -0.0001
## 200 0.1731 nan 0.1000 -0.0002
## 220 0.1539 nan 0.1000 -0.0004
## 240 0.1379 nan 0.1000 -0.0003
## 260 0.1238 nan 0.1000 -0.0006
## 280 0.1101 nan 0.1000 -0.0003
## 300 0.0986 nan 0.1000 0.0000
## 320 0.0884 nan 0.1000 -0.0003
## 340 0.0794 nan 0.1000 -0.0002
## 360 0.0714 nan 0.1000 -0.0001
## 380 0.0647 nan 0.1000 -0.0002
## 400 0.0589 nan 0.1000 -0.0002
## 420 0.0535 nan 0.1000 -0.0002
## 440 0.0485 nan 0.1000 -0.0001
## 460 0.0437 nan 0.1000 -0.0001
## 480 0.0399 nan 0.1000 -0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0382
## 2 1.1642 nan 0.1000 0.0326
## 3 1.1065 nan 0.1000 0.0210
## 4 1.0566 nan 0.1000 0.0186
## 5 1.0150 nan 0.1000 0.0158
## 6 0.9727 nan 0.1000 0.0146
## 7 0.9390 nan 0.1000 0.0155
## 8 0.9113 nan 0.1000 0.0113
## 9 0.8829 nan 0.1000 0.0110
## 10 0.8586 nan 0.1000 0.0095
## 20 0.7028 nan 0.1000 0.0027
## 40 0.5604 nan 0.1000 0.0003
## 60 0.4822 nan 0.1000 -0.0004
## 80 0.4148 nan 0.1000 -0.0008
## 100 0.3607 nan 0.1000 -0.0006
## 120 0.3038 nan 0.1000 -0.0001
## 140 0.2657 nan 0.1000 -0.0005
## 160 0.2327 nan 0.1000 -0.0010
## 180 0.2045 nan 0.1000 -0.0008
## 200 0.1807 nan 0.1000 -0.0002
## 220 0.1590 nan 0.1000 -0.0009
## 240 0.1426 nan 0.1000 -0.0005
## 260 0.1277 nan 0.1000 -0.0006
## 280 0.1143 nan 0.1000 -0.0006
## 300 0.1015 nan 0.1000 -0.0003
## 320 0.0900 nan 0.1000 -0.0003
## 340 0.0813 nan 0.1000 -0.0001
## 360 0.0718 nan 0.1000 -0.0002
## 380 0.0651 nan 0.1000 -0.0002
## 400 0.0586 nan 0.1000 -0.0001
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## 460 0.0436 nan 0.1000 -0.0001
## 480 0.0397 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2396 nan 0.1000 0.0358
## 2 1.1789 nan 0.1000 0.0281
## 3 1.1174 nan 0.1000 0.0267
## 4 1.0672 nan 0.1000 0.0199
## 5 1.0220 nan 0.1000 0.0203
## 6 0.9810 nan 0.1000 0.0191
## 7 0.9399 nan 0.1000 0.0125
## 8 0.9087 nan 0.1000 0.0112
## 9 0.8845 nan 0.1000 0.0079
## 10 0.8552 nan 0.1000 0.0128
## 20 0.7056 nan 0.1000 0.0011
## 40 0.5704 nan 0.1000 -0.0011
## 60 0.4808 nan 0.1000 -0.0011
## 80 0.4099 nan 0.1000 -0.0005
## 100 0.3566 nan 0.1000 -0.0015
## 120 0.3099 nan 0.1000 0.0001
## 140 0.2724 nan 0.1000 -0.0014
## 160 0.2423 nan 0.1000 -0.0015
## 180 0.2183 nan 0.1000 -0.0011
## 200 0.1937 nan 0.1000 0.0001
## 220 0.1743 nan 0.1000 -0.0008
## 240 0.1567 nan 0.1000 -0.0004
## 260 0.1391 nan 0.1000 -0.0006
## 280 0.1241 nan 0.1000 -0.0005
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## 320 0.1019 nan 0.1000 -0.0003
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## 380 0.0767 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.1000 0.0376
## 2 1.1594 nan 0.1000 0.0335
## 3 1.0906 nan 0.1000 0.0306
## 4 1.0344 nan 0.1000 0.0223
## 5 0.9807 nan 0.1000 0.0213
## 6 0.9393 nan 0.1000 0.0149
## 7 0.9021 nan 0.1000 0.0150
## 8 0.8742 nan 0.1000 0.0092
## 9 0.8472 nan 0.1000 0.0085
## 10 0.8208 nan 0.1000 0.0090
## 20 0.6673 nan 0.1000 0.0015
## 40 0.5124 nan 0.1000 0.0003
## 60 0.4152 nan 0.1000 -0.0015
## 80 0.3504 nan 0.1000 -0.0008
## 100 0.2922 nan 0.1000 0.0009
## 120 0.2450 nan 0.1000 0.0000
## 140 0.2089 nan 0.1000 -0.0005
## 160 0.1760 nan 0.1000 -0.0001
## 180 0.1519 nan 0.1000 -0.0006
## 200 0.1317 nan 0.1000 -0.0000
## 220 0.1145 nan 0.1000 -0.0002
## 240 0.0995 nan 0.1000 -0.0002
## 260 0.0869 nan 0.1000 -0.0003
## 280 0.0760 nan 0.1000 -0.0002
## 300 0.0673 nan 0.1000 0.0000
## 320 0.0598 nan 0.1000 -0.0002
## 340 0.0525 nan 0.1000 -0.0001
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## 380 0.0399 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2265 nan 0.1000 0.0401
## 2 1.1588 nan 0.1000 0.0316
## 3 1.0868 nan 0.1000 0.0272
## 4 1.0326 nan 0.1000 0.0219
## 5 0.9892 nan 0.1000 0.0173
## 6 0.9531 nan 0.1000 0.0183
## 7 0.9182 nan 0.1000 0.0127
## 8 0.8882 nan 0.1000 0.0104
## 9 0.8553 nan 0.1000 0.0131
## 10 0.8290 nan 0.1000 0.0100
## 20 0.6713 nan 0.1000 0.0020
## 40 0.5188 nan 0.1000 0.0012
## 60 0.4193 nan 0.1000 0.0000
## 80 0.3447 nan 0.1000 -0.0005
## 100 0.2882 nan 0.1000 -0.0015
## 120 0.2441 nan 0.1000 -0.0007
## 140 0.2083 nan 0.1000 -0.0003
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## 180 0.1549 nan 0.1000 -0.0006
## 200 0.1321 nan 0.1000 -0.0004
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## 240 0.1016 nan 0.1000 -0.0001
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## 280 0.0793 nan 0.1000 -0.0002
## 300 0.0700 nan 0.1000 -0.0005
## 320 0.0615 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2317 nan 0.1000 0.0400
## 2 1.1610 nan 0.1000 0.0317
## 3 1.0991 nan 0.1000 0.0291
## 4 1.0479 nan 0.1000 0.0223
## 5 1.0003 nan 0.1000 0.0179
## 6 0.9550 nan 0.1000 0.0194
## 7 0.9231 nan 0.1000 0.0121
## 8 0.8910 nan 0.1000 0.0126
## 9 0.8598 nan 0.1000 0.0104
## 10 0.8330 nan 0.1000 0.0076
## 20 0.6760 nan 0.1000 0.0016
## 40 0.5283 nan 0.1000 -0.0007
## 60 0.4366 nan 0.1000 -0.0006
## 80 0.3673 nan 0.1000 -0.0011
## 100 0.3103 nan 0.1000 -0.0008
## 120 0.2665 nan 0.1000 -0.0008
## 140 0.2295 nan 0.1000 -0.0008
## 160 0.1961 nan 0.1000 -0.0001
## 180 0.1708 nan 0.1000 -0.0002
## 200 0.1488 nan 0.1000 -0.0003
## 220 0.1296 nan 0.1000 -0.0004
## 240 0.1131 nan 0.1000 -0.0003
## 260 0.1007 nan 0.1000 -0.0008
## 280 0.0867 nan 0.1000 -0.0003
## 300 0.0754 nan 0.1000 -0.0002
## 320 0.0672 nan 0.1000 -0.0001
## 340 0.0590 nan 0.1000 -0.0003
## 360 0.0518 nan 0.1000 -0.0001
## 380 0.0458 nan 0.1000 -0.0001
## 400 0.0409 nan 0.1000 -0.0002
## 420 0.0364 nan 0.1000 -0.0001
## 440 0.0330 nan 0.1000 -0.0001
## 460 0.0293 nan 0.1000 -0.0001
## 480 0.0262 nan 0.1000 -0.0001
## 500 0.0233 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0004
## 2 1.3194 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3140 nan 0.0010 0.0004
## 9 1.3131 nan 0.0010 0.0005
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2698 nan 0.0010 0.0004
## 80 1.2535 nan 0.0010 0.0004
## 100 1.2385 nan 0.0010 0.0004
## 120 1.2235 nan 0.0010 0.0003
## 140 1.2090 nan 0.0010 0.0003
## 160 1.1951 nan 0.0010 0.0003
## 180 1.1818 nan 0.0010 0.0003
## 200 1.1687 nan 0.0010 0.0003
## 220 1.1561 nan 0.0010 0.0003
## 240 1.1442 nan 0.0010 0.0003
## 260 1.1325 nan 0.0010 0.0002
## 280 1.1208 nan 0.0010 0.0003
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## 320 1.0990 nan 0.0010 0.0002
## 340 1.0885 nan 0.0010 0.0002
## 360 1.0782 nan 0.0010 0.0002
## 380 1.0681 nan 0.0010 0.0002
## 400 1.0586 nan 0.0010 0.0002
## 420 1.0495 nan 0.0010 0.0002
## 440 1.0404 nan 0.0010 0.0002
## 460 1.0316 nan 0.0010 0.0002
## 480 1.0232 nan 0.0010 0.0002
## 500 1.0148 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0004
## 2 1.3195 nan 0.0010 0.0004
## 3 1.3186 nan 0.0010 0.0004
## 4 1.3178 nan 0.0010 0.0004
## 5 1.3168 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3151 nan 0.0010 0.0004
## 8 1.3143 nan 0.0010 0.0004
## 9 1.3134 nan 0.0010 0.0004
## 10 1.3125 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2865 nan 0.0010 0.0004
## 60 1.2700 nan 0.0010 0.0004
## 80 1.2540 nan 0.0010 0.0003
## 100 1.2393 nan 0.0010 0.0004
## 120 1.2249 nan 0.0010 0.0004
## 140 1.2106 nan 0.0010 0.0003
## 160 1.1966 nan 0.0010 0.0003
## 180 1.1831 nan 0.0010 0.0003
## 200 1.1702 nan 0.0010 0.0003
## 220 1.1575 nan 0.0010 0.0003
## 240 1.1453 nan 0.0010 0.0003
## 260 1.1338 nan 0.0010 0.0003
## 280 1.1221 nan 0.0010 0.0002
## 300 1.1109 nan 0.0010 0.0003
## 320 1.1001 nan 0.0010 0.0002
## 340 1.0897 nan 0.0010 0.0002
## 360 1.0797 nan 0.0010 0.0002
## 380 1.0696 nan 0.0010 0.0002
## 400 1.0601 nan 0.0010 0.0002
## 420 1.0506 nan 0.0010 0.0002
## 440 1.0416 nan 0.0010 0.0002
## 460 1.0328 nan 0.0010 0.0002
## 480 1.0244 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3204 nan 0.0010 0.0003
## 2 1.3194 nan 0.0010 0.0005
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3176 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3158 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3141 nan 0.0010 0.0004
## 9 1.3132 nan 0.0010 0.0004
## 10 1.3123 nan 0.0010 0.0004
## 20 1.3035 nan 0.0010 0.0004
## 40 1.2864 nan 0.0010 0.0004
## 60 1.2701 nan 0.0010 0.0004
## 80 1.2545 nan 0.0010 0.0003
## 100 1.2393 nan 0.0010 0.0004
## 120 1.2247 nan 0.0010 0.0003
## 140 1.2103 nan 0.0010 0.0003
## 160 1.1971 nan 0.0010 0.0003
## 180 1.1836 nan 0.0010 0.0003
## 200 1.1711 nan 0.0010 0.0003
## 220 1.1586 nan 0.0010 0.0002
## 240 1.1467 nan 0.0010 0.0003
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## 280 1.1238 nan 0.0010 0.0002
## 300 1.1128 nan 0.0010 0.0002
## 320 1.1023 nan 0.0010 0.0003
## 340 1.0918 nan 0.0010 0.0003
## 360 1.0817 nan 0.0010 0.0002
## 380 1.0719 nan 0.0010 0.0002
## 400 1.0625 nan 0.0010 0.0002
## 420 1.0532 nan 0.0010 0.0002
## 440 1.0442 nan 0.0010 0.0002
## 460 1.0355 nan 0.0010 0.0002
## 480 1.0271 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0005
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0004
## 9 1.3125 nan 0.0010 0.0004
## 10 1.3116 nan 0.0010 0.0005
## 20 1.3023 nan 0.0010 0.0004
## 40 1.2842 nan 0.0010 0.0004
## 60 1.2666 nan 0.0010 0.0004
## 80 1.2498 nan 0.0010 0.0003
## 100 1.2337 nan 0.0010 0.0004
## 120 1.2183 nan 0.0010 0.0003
## 140 1.2030 nan 0.0010 0.0003
## 160 1.1884 nan 0.0010 0.0004
## 180 1.1741 nan 0.0010 0.0003
## 200 1.1606 nan 0.0010 0.0003
## 220 1.1477 nan 0.0010 0.0002
## 240 1.1351 nan 0.0010 0.0003
## 260 1.1227 nan 0.0010 0.0003
## 280 1.1106 nan 0.0010 0.0002
## 300 1.0989 nan 0.0010 0.0003
## 320 1.0875 nan 0.0010 0.0002
## 340 1.0765 nan 0.0010 0.0003
## 360 1.0657 nan 0.0010 0.0002
## 380 1.0556 nan 0.0010 0.0002
## 400 1.0455 nan 0.0010 0.0002
## 420 1.0356 nan 0.0010 0.0002
## 440 1.0260 nan 0.0010 0.0002
## 460 1.0166 nan 0.0010 0.0002
## 480 1.0077 nan 0.0010 0.0002
## 500 0.9989 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3145 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0005
## 10 1.3117 nan 0.0010 0.0004
## 20 1.3026 nan 0.0010 0.0004
## 40 1.2845 nan 0.0010 0.0004
## 60 1.2674 nan 0.0010 0.0004
## 80 1.2509 nan 0.0010 0.0004
## 100 1.2350 nan 0.0010 0.0003
## 120 1.2195 nan 0.0010 0.0003
## 140 1.2044 nan 0.0010 0.0004
## 160 1.1898 nan 0.0010 0.0003
## 180 1.1757 nan 0.0010 0.0003
## 200 1.1620 nan 0.0010 0.0003
## 220 1.1486 nan 0.0010 0.0003
## 240 1.1360 nan 0.0010 0.0003
## 260 1.1238 nan 0.0010 0.0003
## 280 1.1117 nan 0.0010 0.0003
## 300 1.1001 nan 0.0010 0.0003
## 320 1.0888 nan 0.0010 0.0002
## 340 1.0778 nan 0.0010 0.0002
## 360 1.0673 nan 0.0010 0.0002
## 380 1.0570 nan 0.0010 0.0003
## 400 1.0466 nan 0.0010 0.0002
## 420 1.0366 nan 0.0010 0.0002
## 440 1.0275 nan 0.0010 0.0002
## 460 1.0181 nan 0.0010 0.0002
## 480 1.0092 nan 0.0010 0.0002
## 500 1.0003 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3185 nan 0.0010 0.0004
## 4 1.3175 nan 0.0010 0.0004
## 5 1.3167 nan 0.0010 0.0004
## 6 1.3159 nan 0.0010 0.0004
## 7 1.3149 nan 0.0010 0.0004
## 8 1.3139 nan 0.0010 0.0004
## 9 1.3130 nan 0.0010 0.0004
## 10 1.3121 nan 0.0010 0.0004
## 20 1.3029 nan 0.0010 0.0004
## 40 1.2851 nan 0.0010 0.0004
## 60 1.2679 nan 0.0010 0.0004
## 80 1.2515 nan 0.0010 0.0004
## 100 1.2356 nan 0.0010 0.0004
## 120 1.2204 nan 0.0010 0.0003
## 140 1.2055 nan 0.0010 0.0004
## 160 1.1907 nan 0.0010 0.0003
## 180 1.1769 nan 0.0010 0.0003
## 200 1.1634 nan 0.0010 0.0003
## 220 1.1507 nan 0.0010 0.0003
## 240 1.1381 nan 0.0010 0.0002
## 260 1.1256 nan 0.0010 0.0003
## 280 1.1139 nan 0.0010 0.0002
## 300 1.1024 nan 0.0010 0.0003
## 320 1.0913 nan 0.0010 0.0002
## 340 1.0806 nan 0.0010 0.0002
## 360 1.0702 nan 0.0010 0.0003
## 380 1.0598 nan 0.0010 0.0002
## 400 1.0498 nan 0.0010 0.0002
## 420 1.0400 nan 0.0010 0.0002
## 440 1.0306 nan 0.0010 0.0002
## 460 1.0216 nan 0.0010 0.0002
## 480 1.0125 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3191 nan 0.0010 0.0005
## 3 1.3181 nan 0.0010 0.0005
## 4 1.3171 nan 0.0010 0.0004
## 5 1.3161 nan 0.0010 0.0005
## 6 1.3151 nan 0.0010 0.0004
## 7 1.3142 nan 0.0010 0.0005
## 8 1.3132 nan 0.0010 0.0004
## 9 1.3123 nan 0.0010 0.0004
## 10 1.3112 nan 0.0010 0.0004
## 20 1.3014 nan 0.0010 0.0004
## 40 1.2827 nan 0.0010 0.0004
## 60 1.2645 nan 0.0010 0.0004
## 80 1.2468 nan 0.0010 0.0003
## 100 1.2303 nan 0.0010 0.0004
## 120 1.2139 nan 0.0010 0.0003
## 140 1.1983 nan 0.0010 0.0004
## 160 1.1831 nan 0.0010 0.0003
## 180 1.1683 nan 0.0010 0.0003
## 200 1.1543 nan 0.0010 0.0003
## 220 1.1405 nan 0.0010 0.0003
## 240 1.1271 nan 0.0010 0.0003
## 260 1.1144 nan 0.0010 0.0003
## 280 1.1020 nan 0.0010 0.0003
## 300 1.0898 nan 0.0010 0.0003
## 320 1.0780 nan 0.0010 0.0003
## 340 1.0666 nan 0.0010 0.0002
## 360 1.0553 nan 0.0010 0.0002
## 380 1.0445 nan 0.0010 0.0002
## 400 1.0340 nan 0.0010 0.0002
## 420 1.0239 nan 0.0010 0.0002
## 440 1.0141 nan 0.0010 0.0002
## 460 1.0044 nan 0.0010 0.0002
## 480 0.9951 nan 0.0010 0.0002
## 500 0.9860 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3203 nan 0.0010 0.0004
## 2 1.3193 nan 0.0010 0.0004
## 3 1.3184 nan 0.0010 0.0004
## 4 1.3174 nan 0.0010 0.0004
## 5 1.3164 nan 0.0010 0.0004
## 6 1.3155 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0005
## 8 1.3135 nan 0.0010 0.0004
## 9 1.3126 nan 0.0010 0.0005
## 10 1.3116 nan 0.0010 0.0004
## 20 1.3018 nan 0.0010 0.0004
## 40 1.2834 nan 0.0010 0.0004
## 60 1.2653 nan 0.0010 0.0004
## 80 1.2483 nan 0.0010 0.0004
## 100 1.2315 nan 0.0010 0.0003
## 120 1.2153 nan 0.0010 0.0004
## 140 1.1999 nan 0.0010 0.0004
## 160 1.1848 nan 0.0010 0.0003
## 180 1.1706 nan 0.0010 0.0003
## 200 1.1565 nan 0.0010 0.0003
## 220 1.1429 nan 0.0010 0.0003
## 240 1.1298 nan 0.0010 0.0003
## 260 1.1172 nan 0.0010 0.0003
## 280 1.1049 nan 0.0010 0.0003
## 300 1.0926 nan 0.0010 0.0002
## 320 1.0807 nan 0.0010 0.0003
## 340 1.0695 nan 0.0010 0.0002
## 360 1.0582 nan 0.0010 0.0002
## 380 1.0477 nan 0.0010 0.0002
## 400 1.0369 nan 0.0010 0.0003
## 420 1.0267 nan 0.0010 0.0002
## 440 1.0167 nan 0.0010 0.0002
## 460 1.0072 nan 0.0010 0.0002
## 480 0.9978 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3202 nan 0.0010 0.0005
## 2 1.3192 nan 0.0010 0.0004
## 3 1.3182 nan 0.0010 0.0005
## 4 1.3173 nan 0.0010 0.0005
## 5 1.3163 nan 0.0010 0.0005
## 6 1.3153 nan 0.0010 0.0004
## 7 1.3144 nan 0.0010 0.0004
## 8 1.3134 nan 0.0010 0.0005
## 9 1.3124 nan 0.0010 0.0005
## 10 1.3115 nan 0.0010 0.0004
## 20 1.3020 nan 0.0010 0.0004
## 40 1.2837 nan 0.0010 0.0004
## 60 1.2662 nan 0.0010 0.0004
## 80 1.2491 nan 0.0010 0.0004
## 100 1.2329 nan 0.0010 0.0004
## 120 1.2170 nan 0.0010 0.0004
## 140 1.2015 nan 0.0010 0.0003
## 160 1.1866 nan 0.0010 0.0003
## 180 1.1721 nan 0.0010 0.0003
## 200 1.1581 nan 0.0010 0.0003
## 220 1.1448 nan 0.0010 0.0003
## 240 1.1318 nan 0.0010 0.0003
## 260 1.1192 nan 0.0010 0.0002
## 280 1.1070 nan 0.0010 0.0003
## 300 1.0954 nan 0.0010 0.0002
## 320 1.0840 nan 0.0010 0.0002
## 340 1.0727 nan 0.0010 0.0002
## 360 1.0621 nan 0.0010 0.0002
## 380 1.0515 nan 0.0010 0.0002
## 400 1.0412 nan 0.0010 0.0002
## 420 1.0312 nan 0.0010 0.0002
## 440 1.0216 nan 0.0010 0.0002
## 460 1.0122 nan 0.0010 0.0002
## 480 1.0031 nan 0.0010 0.0002
## 500 0.9940 nan 0.0010 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3120 nan 0.0100 0.0044
## 2 1.3040 nan 0.0100 0.0033
## 3 1.2946 nan 0.0100 0.0045
## 4 1.2860 nan 0.0100 0.0036
## 5 1.2770 nan 0.0100 0.0042
## 6 1.2686 nan 0.0100 0.0039
## 7 1.2610 nan 0.0100 0.0035
## 8 1.2536 nan 0.0100 0.0038
## 9 1.2461 nan 0.0100 0.0034
## 10 1.2385 nan 0.0100 0.0037
## 20 1.1692 nan 0.0100 0.0031
## 40 1.0587 nan 0.0100 0.0021
## 60 0.9767 nan 0.0100 0.0016
## 80 0.9117 nan 0.0100 0.0011
## 100 0.8619 nan 0.0100 0.0006
## 120 0.8197 nan 0.0100 0.0006
## 140 0.7844 nan 0.0100 0.0004
## 160 0.7542 nan 0.0100 0.0004
## 180 0.7301 nan 0.0100 0.0004
## 200 0.7076 nan 0.0100 0.0005
## 220 0.6885 nan 0.0100 0.0004
## 240 0.6715 nan 0.0100 0.0001
## 260 0.6558 nan 0.0100 0.0000
## 280 0.6415 nan 0.0100 0.0002
## 300 0.6290 nan 0.0100 -0.0000
## 320 0.6164 nan 0.0100 0.0002
## 340 0.6046 nan 0.0100 -0.0000
## 360 0.5940 nan 0.0100 0.0001
## 380 0.5837 nan 0.0100 -0.0001
## 400 0.5738 nan 0.0100 -0.0001
## 420 0.5646 nan 0.0100 0.0000
## 440 0.5558 nan 0.0100 -0.0000
## 460 0.5472 nan 0.0100 0.0001
## 480 0.5386 nan 0.0100 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0046
## 2 1.3036 nan 0.0100 0.0040
## 3 1.2956 nan 0.0100 0.0035
## 4 1.2875 nan 0.0100 0.0042
## 5 1.2794 nan 0.0100 0.0034
## 6 1.2710 nan 0.0100 0.0038
## 7 1.2624 nan 0.0100 0.0038
## 8 1.2548 nan 0.0100 0.0038
## 9 1.2461 nan 0.0100 0.0037
## 10 1.2386 nan 0.0100 0.0036
## 20 1.1680 nan 0.0100 0.0029
## 40 1.0590 nan 0.0100 0.0023
## 60 0.9772 nan 0.0100 0.0014
## 80 0.9110 nan 0.0100 0.0012
## 100 0.8609 nan 0.0100 0.0007
## 120 0.8194 nan 0.0100 0.0005
## 140 0.7858 nan 0.0100 0.0003
## 160 0.7563 nan 0.0100 0.0006
## 180 0.7306 nan 0.0100 0.0001
## 200 0.7091 nan 0.0100 0.0002
## 220 0.6897 nan 0.0100 0.0003
## 240 0.6731 nan 0.0100 0.0001
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## 300 0.6297 nan 0.0100 0.0001
## 320 0.6181 nan 0.0100 0.0001
## 340 0.6065 nan 0.0100 0.0001
## 360 0.5969 nan 0.0100 -0.0001
## 380 0.5868 nan 0.0100 -0.0002
## 400 0.5775 nan 0.0100 0.0001
## 420 0.5682 nan 0.0100 0.0000
## 440 0.5594 nan 0.0100 -0.0001
## 460 0.5516 nan 0.0100 -0.0000
## 480 0.5434 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3117 nan 0.0100 0.0045
## 2 1.3026 nan 0.0100 0.0042
## 3 1.2946 nan 0.0100 0.0039
## 4 1.2860 nan 0.0100 0.0041
## 5 1.2778 nan 0.0100 0.0037
## 6 1.2690 nan 0.0100 0.0040
## 7 1.2615 nan 0.0100 0.0036
## 8 1.2548 nan 0.0100 0.0032
## 9 1.2471 nan 0.0100 0.0034
## 10 1.2394 nan 0.0100 0.0034
## 20 1.1717 nan 0.0100 0.0029
## 40 1.0651 nan 0.0100 0.0016
## 60 0.9820 nan 0.0100 0.0012
## 80 0.9186 nan 0.0100 0.0014
## 100 0.8670 nan 0.0100 0.0010
## 120 0.8245 nan 0.0100 0.0006
## 140 0.7899 nan 0.0100 0.0006
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## 180 0.7379 nan 0.0100 0.0003
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## 220 0.6966 nan 0.0100 0.0001
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## 280 0.6505 nan 0.0100 -0.0001
## 300 0.6372 nan 0.0100 0.0002
## 320 0.6235 nan 0.0100 0.0001
## 340 0.6121 nan 0.0100 -0.0000
## 360 0.6014 nan 0.0100 0.0002
## 380 0.5911 nan 0.0100 -0.0001
## 400 0.5819 nan 0.0100 0.0001
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## 460 0.5557 nan 0.0100 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0046
## 2 1.3014 nan 0.0100 0.0048
## 3 1.2918 nan 0.0100 0.0044
## 4 1.2833 nan 0.0100 0.0040
## 5 1.2749 nan 0.0100 0.0037
## 6 1.2664 nan 0.0100 0.0038
## 7 1.2576 nan 0.0100 0.0040
## 8 1.2487 nan 0.0100 0.0038
## 9 1.2403 nan 0.0100 0.0038
## 10 1.2323 nan 0.0100 0.0036
## 20 1.1592 nan 0.0100 0.0032
## 40 1.0439 nan 0.0100 0.0020
## 60 0.9579 nan 0.0100 0.0016
## 80 0.8900 nan 0.0100 0.0011
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## 340 0.5616 nan 0.0100 0.0000
## 360 0.5493 nan 0.0100 -0.0000
## 380 0.5374 nan 0.0100 -0.0000
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## 480 0.4883 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0043
## 2 1.3028 nan 0.0100 0.0045
## 3 1.2932 nan 0.0100 0.0045
## 4 1.2850 nan 0.0100 0.0038
## 5 1.2759 nan 0.0100 0.0041
## 6 1.2680 nan 0.0100 0.0038
## 7 1.2594 nan 0.0100 0.0041
## 8 1.2510 nan 0.0100 0.0039
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## 10 1.2351 nan 0.0100 0.0032
## 20 1.1608 nan 0.0100 0.0030
## 40 1.0456 nan 0.0100 0.0018
## 60 0.9587 nan 0.0100 0.0015
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## 140 0.7582 nan 0.0100 0.0007
## 160 0.7273 nan 0.0100 0.0004
## 180 0.7019 nan 0.0100 0.0002
## 200 0.6781 nan 0.0100 0.0003
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## 240 0.6409 nan 0.0100 -0.0000
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## 320 0.5821 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3115 nan 0.0100 0.0043
## 2 1.3036 nan 0.0100 0.0034
## 3 1.2947 nan 0.0100 0.0041
## 4 1.2861 nan 0.0100 0.0036
## 5 1.2774 nan 0.0100 0.0043
## 6 1.2690 nan 0.0100 0.0042
## 7 1.2605 nan 0.0100 0.0041
## 8 1.2521 nan 0.0100 0.0038
## 9 1.2445 nan 0.0100 0.0031
## 10 1.2367 nan 0.0100 0.0036
## 20 1.1644 nan 0.0100 0.0033
## 40 1.0503 nan 0.0100 0.0023
## 60 0.9639 nan 0.0100 0.0017
## 80 0.8981 nan 0.0100 0.0012
## 100 0.8440 nan 0.0100 0.0011
## 120 0.8016 nan 0.0100 0.0006
## 140 0.7649 nan 0.0100 0.0004
## 160 0.7340 nan 0.0100 0.0004
## 180 0.7088 nan 0.0100 0.0002
## 200 0.6844 nan 0.0100 0.0002
## 220 0.6655 nan 0.0100 0.0001
## 240 0.6473 nan 0.0100 -0.0000
## 260 0.6305 nan 0.0100 0.0001
## 280 0.6157 nan 0.0100 0.0002
## 300 0.6005 nan 0.0100 -0.0000
## 320 0.5867 nan 0.0100 -0.0001
## 340 0.5752 nan 0.0100 0.0000
## 360 0.5634 nan 0.0100 -0.0001
## 380 0.5515 nan 0.0100 0.0000
## 400 0.5410 nan 0.0100 -0.0000
## 420 0.5303 nan 0.0100 0.0001
## 440 0.5210 nan 0.0100 0.0001
## 460 0.5121 nan 0.0100 -0.0001
## 480 0.5024 nan 0.0100 0.0001
## 500 0.4937 nan 0.0100 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3114 nan 0.0100 0.0047
## 2 1.3020 nan 0.0100 0.0047
## 3 1.2927 nan 0.0100 0.0043
## 4 1.2833 nan 0.0100 0.0043
## 5 1.2747 nan 0.0100 0.0037
## 6 1.2650 nan 0.0100 0.0045
## 7 1.2558 nan 0.0100 0.0039
## 8 1.2482 nan 0.0100 0.0034
## 9 1.2389 nan 0.0100 0.0041
## 10 1.2308 nan 0.0100 0.0035
## 20 1.1535 nan 0.0100 0.0032
## 40 1.0325 nan 0.0100 0.0020
## 60 0.9402 nan 0.0100 0.0018
## 80 0.8687 nan 0.0100 0.0012
## 100 0.8111 nan 0.0100 0.0011
## 120 0.7633 nan 0.0100 0.0008
## 140 0.7247 nan 0.0100 0.0006
## 160 0.6909 nan 0.0100 0.0005
## 180 0.6629 nan 0.0100 0.0005
## 200 0.6384 nan 0.0100 0.0002
## 220 0.6170 nan 0.0100 0.0003
## 240 0.5969 nan 0.0100 -0.0000
## 260 0.5802 nan 0.0100 0.0002
## 280 0.5622 nan 0.0100 0.0001
## 300 0.5470 nan 0.0100 0.0001
## 320 0.5329 nan 0.0100 0.0001
## 340 0.5189 nan 0.0100 0.0000
## 360 0.5061 nan 0.0100 0.0001
## 380 0.4944 nan 0.0100 0.0000
## 400 0.4826 nan 0.0100 0.0000
## 420 0.4720 nan 0.0100 0.0000
## 440 0.4610 nan 0.0100 -0.0001
## 460 0.4509 nan 0.0100 -0.0000
## 480 0.4407 nan 0.0100 -0.0001
## 500 0.4307 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0040
## 2 1.3033 nan 0.0100 0.0035
## 3 1.2934 nan 0.0100 0.0047
## 4 1.2845 nan 0.0100 0.0038
## 5 1.2750 nan 0.0100 0.0042
## 6 1.2661 nan 0.0100 0.0039
## 7 1.2559 nan 0.0100 0.0043
## 8 1.2473 nan 0.0100 0.0040
## 9 1.2389 nan 0.0100 0.0040
## 10 1.2309 nan 0.0100 0.0037
## 20 1.1554 nan 0.0100 0.0028
## 40 1.0370 nan 0.0100 0.0026
## 60 0.9488 nan 0.0100 0.0019
## 80 0.8780 nan 0.0100 0.0016
## 100 0.8217 nan 0.0100 0.0009
## 120 0.7750 nan 0.0100 0.0006
## 140 0.7369 nan 0.0100 0.0008
## 160 0.7039 nan 0.0100 0.0002
## 180 0.6760 nan 0.0100 0.0003
## 200 0.6518 nan 0.0100 0.0003
## 220 0.6301 nan 0.0100 0.0002
## 240 0.6096 nan 0.0100 -0.0001
## 260 0.5912 nan 0.0100 0.0001
## 280 0.5742 nan 0.0100 0.0000
## 300 0.5580 nan 0.0100 0.0000
## 320 0.5421 nan 0.0100 0.0000
## 340 0.5284 nan 0.0100 0.0000
## 360 0.5160 nan 0.0100 -0.0000
## 380 0.5032 nan 0.0100 0.0001
## 400 0.4917 nan 0.0100 0.0001
## 420 0.4808 nan 0.0100 -0.0001
## 440 0.4704 nan 0.0100 0.0000
## 460 0.4597 nan 0.0100 0.0001
## 480 0.4501 nan 0.0100 -0.0000
## 500 0.4391 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3119 nan 0.0100 0.0044
## 2 1.3027 nan 0.0100 0.0041
## 3 1.2933 nan 0.0100 0.0045
## 4 1.2842 nan 0.0100 0.0042
## 5 1.2754 nan 0.0100 0.0039
## 6 1.2662 nan 0.0100 0.0037
## 7 1.2573 nan 0.0100 0.0043
## 8 1.2483 nan 0.0100 0.0040
## 9 1.2395 nan 0.0100 0.0036
## 10 1.2311 nan 0.0100 0.0039
## 20 1.1562 nan 0.0100 0.0034
## 40 1.0399 nan 0.0100 0.0024
## 60 0.9510 nan 0.0100 0.0017
## 80 0.8818 nan 0.0100 0.0013
## 100 0.8256 nan 0.0100 0.0011
## 120 0.7812 nan 0.0100 0.0009
## 140 0.7440 nan 0.0100 0.0008
## 160 0.7105 nan 0.0100 0.0005
## 180 0.6821 nan 0.0100 0.0002
## 200 0.6574 nan 0.0100 0.0001
## 220 0.6354 nan 0.0100 0.0000
## 240 0.6158 nan 0.0100 0.0000
## 260 0.5980 nan 0.0100 0.0000
## 280 0.5808 nan 0.0100 0.0001
## 300 0.5662 nan 0.0100 -0.0000
## 320 0.5524 nan 0.0100 0.0001
## 340 0.5394 nan 0.0100 -0.0001
## 360 0.5270 nan 0.0100 -0.0001
## 380 0.5156 nan 0.0100 0.0000
## 400 0.5049 nan 0.0100 -0.0001
## 420 0.4944 nan 0.0100 -0.0002
## 440 0.4832 nan 0.0100 -0.0001
## 460 0.4729 nan 0.0100 -0.0003
## 480 0.4634 nan 0.0100 -0.0002
## 500 0.4533 nan 0.0100 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0391
## 2 1.1606 nan 0.1000 0.0317
## 3 1.0978 nan 0.1000 0.0281
## 4 1.0456 nan 0.1000 0.0233
## 5 1.0038 nan 0.1000 0.0177
## 6 0.9669 nan 0.1000 0.0171
## 7 0.9352 nan 0.1000 0.0116
## 8 0.9046 nan 0.1000 0.0111
## 9 0.8770 nan 0.1000 0.0095
## 10 0.8480 nan 0.1000 0.0126
## 20 0.7049 nan 0.1000 0.0005
## 40 0.5837 nan 0.1000 -0.0007
## 60 0.5097 nan 0.1000 -0.0007
## 80 0.4429 nan 0.1000 -0.0013
## 100 0.3870 nan 0.1000 0.0001
## 120 0.3438 nan 0.1000 -0.0009
## 140 0.3103 nan 0.1000 -0.0014
## 160 0.2791 nan 0.1000 0.0000
## 180 0.2534 nan 0.1000 -0.0011
## 200 0.2256 nan 0.1000 -0.0009
## 220 0.2045 nan 0.1000 -0.0009
## 240 0.1859 nan 0.1000 -0.0001
## 260 0.1669 nan 0.1000 -0.0002
## 280 0.1517 nan 0.1000 -0.0004
## 300 0.1372 nan 0.1000 -0.0003
## 320 0.1264 nan 0.1000 -0.0002
## 340 0.1167 nan 0.1000 -0.0002
## 360 0.1061 nan 0.1000 -0.0002
## 380 0.0975 nan 0.1000 -0.0003
## 400 0.0899 nan 0.1000 -0.0001
## 420 0.0829 nan 0.1000 -0.0003
## 440 0.0769 nan 0.1000 -0.0001
## 460 0.0712 nan 0.1000 -0.0002
## 480 0.0653 nan 0.1000 0.0000
## 500 0.0603 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0399
## 2 1.1595 nan 0.1000 0.0325
## 3 1.1030 nan 0.1000 0.0269
## 4 1.0548 nan 0.1000 0.0230
## 5 1.0085 nan 0.1000 0.0225
## 6 0.9741 nan 0.1000 0.0146
## 7 0.9410 nan 0.1000 0.0144
## 8 0.9134 nan 0.1000 0.0087
## 9 0.8853 nan 0.1000 0.0112
## 10 0.8645 nan 0.1000 0.0084
## 20 0.7108 nan 0.1000 0.0033
## 40 0.5788 nan 0.1000 0.0001
## 60 0.4987 nan 0.1000 -0.0003
## 80 0.4349 nan 0.1000 -0.0011
## 100 0.3878 nan 0.1000 -0.0011
## 120 0.3422 nan 0.1000 -0.0004
## 140 0.3096 nan 0.1000 -0.0011
## 160 0.2807 nan 0.1000 0.0003
## 180 0.2535 nan 0.1000 -0.0007
## 200 0.2304 nan 0.1000 -0.0007
## 220 0.2068 nan 0.1000 -0.0003
## 240 0.1883 nan 0.1000 -0.0011
## 260 0.1717 nan 0.1000 -0.0003
## 280 0.1568 nan 0.1000 -0.0005
## 300 0.1434 nan 0.1000 -0.0004
## 320 0.1328 nan 0.1000 -0.0003
## 340 0.1241 nan 0.1000 -0.0001
## 360 0.1148 nan 0.1000 -0.0002
## 380 0.1060 nan 0.1000 -0.0003
## 400 0.0975 nan 0.1000 -0.0002
## 420 0.0880 nan 0.1000 -0.0000
## 440 0.0808 nan 0.1000 -0.0003
## 460 0.0752 nan 0.1000 -0.0004
## 480 0.0691 nan 0.1000 -0.0001
## 500 0.0643 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0387
## 2 1.1681 nan 0.1000 0.0315
## 3 1.1086 nan 0.1000 0.0276
## 4 1.0559 nan 0.1000 0.0227
## 5 1.0135 nan 0.1000 0.0195
## 6 0.9750 nan 0.1000 0.0155
## 7 0.9468 nan 0.1000 0.0091
## 8 0.9178 nan 0.1000 0.0130
## 9 0.8926 nan 0.1000 0.0113
## 10 0.8674 nan 0.1000 0.0093
## 20 0.7199 nan 0.1000 0.0020
## 40 0.5855 nan 0.1000 -0.0010
## 60 0.5058 nan 0.1000 -0.0014
## 80 0.4462 nan 0.1000 -0.0018
## 100 0.3944 nan 0.1000 -0.0012
## 120 0.3542 nan 0.1000 -0.0008
## 140 0.3202 nan 0.1000 -0.0009
## 160 0.2908 nan 0.1000 -0.0008
## 180 0.2625 nan 0.1000 -0.0004
## 200 0.2425 nan 0.1000 -0.0008
## 220 0.2244 nan 0.1000 -0.0010
## 240 0.2047 nan 0.1000 -0.0006
## 260 0.1891 nan 0.1000 -0.0006
## 280 0.1747 nan 0.1000 -0.0009
## 300 0.1615 nan 0.1000 -0.0005
## 320 0.1487 nan 0.1000 -0.0005
## 340 0.1354 nan 0.1000 -0.0007
## 360 0.1250 nan 0.1000 -0.0007
## 380 0.1162 nan 0.1000 -0.0003
## 400 0.1081 nan 0.1000 -0.0005
## 420 0.1006 nan 0.1000 -0.0004
## 440 0.0931 nan 0.1000 -0.0006
## 460 0.0863 nan 0.1000 -0.0001
## 480 0.0809 nan 0.1000 -0.0001
## 500 0.0742 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2326 nan 0.1000 0.0431
## 2 1.1602 nan 0.1000 0.0308
## 3 1.1046 nan 0.1000 0.0218
## 4 1.0478 nan 0.1000 0.0257
## 5 1.0046 nan 0.1000 0.0194
## 6 0.9625 nan 0.1000 0.0185
## 7 0.9257 nan 0.1000 0.0149
## 8 0.8919 nan 0.1000 0.0128
## 9 0.8662 nan 0.1000 0.0117
## 10 0.8390 nan 0.1000 0.0110
## 20 0.6817 nan 0.1000 -0.0007
## 40 0.5341 nan 0.1000 0.0010
## 60 0.4454 nan 0.1000 -0.0005
## 80 0.3794 nan 0.1000 -0.0002
## 100 0.3291 nan 0.1000 -0.0006
## 120 0.2870 nan 0.1000 -0.0005
## 140 0.2507 nan 0.1000 -0.0005
## 160 0.2185 nan 0.1000 -0.0004
## 180 0.1928 nan 0.1000 -0.0002
## 200 0.1712 nan 0.1000 -0.0005
## 220 0.1523 nan 0.1000 -0.0004
## 240 0.1371 nan 0.1000 -0.0008
## 260 0.1223 nan 0.1000 -0.0002
## 280 0.1098 nan 0.1000 -0.0002
## 300 0.0980 nan 0.1000 -0.0001
## 320 0.0880 nan 0.1000 -0.0003
## 340 0.0804 nan 0.1000 -0.0000
## 360 0.0727 nan 0.1000 -0.0001
## 380 0.0661 nan 0.1000 -0.0000
## 400 0.0596 nan 0.1000 -0.0002
## 420 0.0529 nan 0.1000 0.0001
## 440 0.0478 nan 0.1000 -0.0001
## 460 0.0428 nan 0.1000 0.0000
## 480 0.0385 nan 0.1000 -0.0001
## 500 0.0350 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2375 nan 0.1000 0.0352
## 2 1.1547 nan 0.1000 0.0362
## 3 1.0940 nan 0.1000 0.0279
## 4 1.0366 nan 0.1000 0.0246
## 5 0.9896 nan 0.1000 0.0187
## 6 0.9509 nan 0.1000 0.0166
## 7 0.9207 nan 0.1000 0.0122
## 8 0.8876 nan 0.1000 0.0124
## 9 0.8620 nan 0.1000 0.0083
## 10 0.8374 nan 0.1000 0.0098
## 20 0.6832 nan 0.1000 0.0026
## 40 0.5446 nan 0.1000 -0.0011
## 60 0.4563 nan 0.1000 -0.0006
## 80 0.3913 nan 0.1000 -0.0008
## 100 0.3390 nan 0.1000 -0.0005
## 120 0.2964 nan 0.1000 -0.0007
## 140 0.2608 nan 0.1000 -0.0007
## 160 0.2268 nan 0.1000 -0.0007
## 180 0.2007 nan 0.1000 -0.0009
## 200 0.1770 nan 0.1000 -0.0004
## 220 0.1558 nan 0.1000 -0.0002
## 240 0.1390 nan 0.1000 -0.0006
## 260 0.1236 nan 0.1000 -0.0010
## 280 0.1102 nan 0.1000 -0.0008
## 300 0.0994 nan 0.1000 -0.0005
## 320 0.0890 nan 0.1000 -0.0003
## 340 0.0795 nan 0.1000 -0.0002
## 360 0.0719 nan 0.1000 -0.0003
## 380 0.0643 nan 0.1000 -0.0004
## 400 0.0574 nan 0.1000 -0.0003
## 420 0.0526 nan 0.1000 -0.0003
## 440 0.0472 nan 0.1000 -0.0001
## 460 0.0427 nan 0.1000 -0.0000
## 480 0.0390 nan 0.1000 -0.0001
## 500 0.0351 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2333 nan 0.1000 0.0440
## 2 1.1622 nan 0.1000 0.0343
## 3 1.0991 nan 0.1000 0.0256
## 4 1.0473 nan 0.1000 0.0222
## 5 1.0025 nan 0.1000 0.0196
## 6 0.9601 nan 0.1000 0.0173
## 7 0.9255 nan 0.1000 0.0157
## 8 0.8968 nan 0.1000 0.0102
## 9 0.8702 nan 0.1000 0.0097
## 10 0.8477 nan 0.1000 0.0060
## 20 0.6818 nan 0.1000 0.0040
## 40 0.5470 nan 0.1000 -0.0013
## 60 0.4629 nan 0.1000 -0.0014
## 80 0.4006 nan 0.1000 -0.0024
## 100 0.3492 nan 0.1000 -0.0012
## 120 0.3049 nan 0.1000 -0.0013
## 140 0.2705 nan 0.1000 -0.0007
## 160 0.2415 nan 0.1000 -0.0002
## 180 0.2169 nan 0.1000 -0.0008
## 200 0.1946 nan 0.1000 -0.0006
## 220 0.1722 nan 0.1000 -0.0009
## 240 0.1532 nan 0.1000 -0.0001
## 260 0.1364 nan 0.1000 -0.0002
## 280 0.1240 nan 0.1000 -0.0004
## 300 0.1113 nan 0.1000 -0.0003
## 320 0.1005 nan 0.1000 -0.0001
## 340 0.0902 nan 0.1000 -0.0003
## 360 0.0828 nan 0.1000 -0.0002
## 380 0.0759 nan 0.1000 -0.0004
## 400 0.0691 nan 0.1000 -0.0004
## 420 0.0620 nan 0.1000 -0.0002
## 440 0.0565 nan 0.1000 -0.0002
## 460 0.0522 nan 0.1000 -0.0001
## 480 0.0473 nan 0.1000 -0.0002
## 500 0.0435 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2293 nan 0.1000 0.0420
## 2 1.1573 nan 0.1000 0.0318
## 3 1.0855 nan 0.1000 0.0292
## 4 1.0321 nan 0.1000 0.0216
## 5 0.9887 nan 0.1000 0.0177
## 6 0.9443 nan 0.1000 0.0136
## 7 0.9071 nan 0.1000 0.0154
## 8 0.8746 nan 0.1000 0.0138
## 9 0.8457 nan 0.1000 0.0106
## 10 0.8195 nan 0.1000 0.0106
## 20 0.6463 nan 0.1000 0.0043
## 40 0.4960 nan 0.1000 -0.0024
## 60 0.3977 nan 0.1000 0.0001
## 80 0.3306 nan 0.1000 0.0012
## 100 0.2779 nan 0.1000 -0.0009
## 120 0.2350 nan 0.1000 -0.0004
## 140 0.2028 nan 0.1000 -0.0005
## 160 0.1751 nan 0.1000 -0.0005
## 180 0.1494 nan 0.1000 -0.0000
## 200 0.1296 nan 0.1000 -0.0003
## 220 0.1155 nan 0.1000 -0.0003
## 240 0.1019 nan 0.1000 -0.0002
## 260 0.0899 nan 0.1000 -0.0000
## 280 0.0789 nan 0.1000 -0.0005
## 300 0.0695 nan 0.1000 -0.0002
## 320 0.0606 nan 0.1000 -0.0003
## 340 0.0536 nan 0.1000 -0.0002
## 360 0.0465 nan 0.1000 -0.0001
## 380 0.0417 nan 0.1000 -0.0002
## 400 0.0366 nan 0.1000 -0.0000
## 420 0.0321 nan 0.1000 -0.0001
## 440 0.0282 nan 0.1000 -0.0001
## 460 0.0251 nan 0.1000 -0.0002
## 480 0.0225 nan 0.1000 -0.0000
## 500 0.0201 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2276 nan 0.1000 0.0409
## 2 1.1466 nan 0.1000 0.0324
## 3 1.0798 nan 0.1000 0.0314
## 4 1.0183 nan 0.1000 0.0278
## 5 0.9667 nan 0.1000 0.0212
## 6 0.9265 nan 0.1000 0.0152
## 7 0.8925 nan 0.1000 0.0155
## 8 0.8595 nan 0.1000 0.0140
## 9 0.8350 nan 0.1000 0.0076
## 10 0.8081 nan 0.1000 0.0093
## 20 0.6393 nan 0.1000 0.0024
## 40 0.4879 nan 0.1000 0.0008
## 60 0.3943 nan 0.1000 0.0005
## 80 0.3298 nan 0.1000 0.0004
## 100 0.2823 nan 0.1000 -0.0006
## 120 0.2415 nan 0.1000 -0.0008
## 140 0.2050 nan 0.1000 -0.0009
## 160 0.1751 nan 0.1000 -0.0007
## 180 0.1517 nan 0.1000 -0.0008
## 200 0.1305 nan 0.1000 -0.0001
## 220 0.1140 nan 0.1000 0.0001
## 240 0.1000 nan 0.1000 -0.0002
## 260 0.0879 nan 0.1000 -0.0003
## 280 0.0776 nan 0.1000 -0.0003
## 300 0.0673 nan 0.1000 -0.0002
## 320 0.0597 nan 0.1000 -0.0003
## 340 0.0531 nan 0.1000 -0.0003
## 360 0.0473 nan 0.1000 -0.0002
## 380 0.0416 nan 0.1000 -0.0001
## 400 0.0365 nan 0.1000 -0.0001
## 420 0.0320 nan 0.1000 -0.0001
## 440 0.0280 nan 0.1000 -0.0001
## 460 0.0248 nan 0.1000 -0.0001
## 480 0.0219 nan 0.1000 -0.0001
## 500 0.0194 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2366 nan 0.1000 0.0385
## 2 1.1584 nan 0.1000 0.0368
## 3 1.0991 nan 0.1000 0.0273
## 4 1.0466 nan 0.1000 0.0222
## 5 0.9926 nan 0.1000 0.0247
## 6 0.9464 nan 0.1000 0.0195
## 7 0.9062 nan 0.1000 0.0151
## 8 0.8731 nan 0.1000 0.0148
## 9 0.8407 nan 0.1000 0.0122
## 10 0.8173 nan 0.1000 0.0095
## 20 0.6617 nan 0.1000 0.0025
## 40 0.5077 nan 0.1000 -0.0001
## 60 0.4201 nan 0.1000 0.0006
## 80 0.3559 nan 0.1000 -0.0011
## 100 0.3013 nan 0.1000 -0.0011
## 120 0.2559 nan 0.1000 -0.0008
## 140 0.2232 nan 0.1000 -0.0000
## 160 0.1944 nan 0.1000 -0.0004
## 180 0.1702 nan 0.1000 -0.0008
## 200 0.1483 nan 0.1000 -0.0007
## 220 0.1276 nan 0.1000 -0.0007
## 240 0.1111 nan 0.1000 -0.0002
## 260 0.0993 nan 0.1000 -0.0004
## 280 0.0867 nan 0.1000 -0.0003
## 300 0.0761 nan 0.1000 -0.0002
## 320 0.0675 nan 0.1000 -0.0002
## 340 0.0598 nan 0.1000 -0.0002
## 360 0.0535 nan 0.1000 -0.0001
## 380 0.0476 nan 0.1000 -0.0003
## 400 0.0421 nan 0.1000 -0.0002
## 420 0.0371 nan 0.1000 -0.0002
## 440 0.0327 nan 0.1000 -0.0002
## 460 0.0292 nan 0.1000 -0.0001
## 480 0.0264 nan 0.1000 -0.0002
## 500 0.0234 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2237 nan 0.1000 0.0446
## 2 1.1491 nan 0.1000 0.0353
## 3 1.0814 nan 0.1000 0.0290
## 4 1.0278 nan 0.1000 0.0217
## 5 0.9859 nan 0.1000 0.0156
## 6 0.9456 nan 0.1000 0.0164
## 7 0.9088 nan 0.1000 0.0144
## 8 0.8745 nan 0.1000 0.0144
## 9 0.8434 nan 0.1000 0.0122
## 10 0.8206 nan 0.1000 0.0090
## 20 0.6571 nan 0.1000 0.0038
## 40 0.5150 nan 0.1000 -0.0013
## 60 0.4277 nan 0.1000 0.0004
## 80 0.3509 nan 0.1000 -0.0002
## 100 0.2923 nan 0.1000 0.0003
## 120 0.2494 nan 0.1000 -0.0002
## 140 0.2124 nan 0.1000 0.0002
## 160 0.1827 nan 0.1000 0.0003
## 180 0.1574 nan 0.1000 -0.0003
## 200 0.1391 nan 0.1000 -0.0002
## 220 0.1235 nan 0.1000 -0.0003
## 240 0.1105 nan 0.1000 -0.0001
## 260 0.0973 nan 0.1000 -0.0000
## 280 0.0851 nan 0.1000 -0.0001
## 300 0.0753 nan 0.1000 -0.0002
## 320 0.0673 nan 0.1000 -0.0001
## 340 0.0593 nan 0.1000 -0.0000
## 360 0.0528 nan 0.1000 -0.0001
## 380 0.0470 nan 0.1000 -0.0001
## 400 0.0418 nan 0.1000 -0.0001
## 420 0.0372 nan 0.1000 -0.0001
## 440 0.0334 nan 0.1000 0.0000
## 460 0.0297 nan 0.1000 -0.0001
## 480 0.0266 nan 0.1000 -0.0000
## 500 0.0237 nan 0.1000 -0.0000
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_GBM_Tune## Stochastic Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode ROC Sens Spec
## 0.001 4 5 0.8910798 0.5294118 0.9513928
## 0.001 4 10 0.8914021 0.5276471 0.9524424
## 0.001 4 15 0.8897511 0.5211765 0.9545294
## 0.001 5 5 0.8958001 0.5658824 0.9478871
## 0.001 5 10 0.8958208 0.5658824 0.9478932
## 0.001 5 15 0.8941702 0.5547059 0.9517346
## 0.001 6 5 0.9005384 0.5864706 0.9485828
## 0.001 6 10 0.8994397 0.5870588 0.9499832
## 0.001 6 15 0.8974198 0.5688235 0.9496384
## 0.010 4 5 0.9185276 0.7723529 0.9087231
## 0.010 4 10 0.9165290 0.7705882 0.9090740
## 0.010 4 15 0.9153323 0.7617647 0.9104683
## 0.010 5 5 0.9237008 0.7764706 0.9094279
## 0.010 5 10 0.9228961 0.7741176 0.9111701
## 0.010 5 15 0.9216394 0.7682353 0.9115271
## 0.010 6 5 0.9296301 0.7852941 0.9132662
## 0.010 6 10 0.9281216 0.7794118 0.9146606
## 0.010 6 15 0.9263270 0.7835294 0.9164119
## 0.100 4 5 0.9573128 0.8964706 0.9359847
## 0.100 4 10 0.9550397 0.8994118 0.9401892
## 0.100 4 15 0.9547391 0.8947059 0.9450801
## 0.100 5 5 0.9584462 0.8958824 0.9408879
## 0.100 5 10 0.9581581 0.8976471 0.9443753
## 0.100 5 15 0.9566970 0.8964706 0.9436949
## 0.100 6 5 0.9599595 0.8970588 0.9405370
## 0.100 6 10 0.9594183 0.8964706 0.9443905
## 0.100 6 15 0.9576763 0.8917647 0.9471762
##
## Tuning parameter 'n.trees' was held constant at a value of 500
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 500, interaction.depth =
## 6, shrinkage = 0.1 and n.minobsinnode = 5.
MBS_GBM_Tune$finalModel## A gradient boosted model with bernoulli loss function.
## 500 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.
MBS_GBM_Tune$results## shrinkage interaction.depth n.minobsinnode n.trees ROC Sens
## 1 0.001 4 5 500 0.8910798 0.5294118
## 2 0.001 4 10 500 0.8914021 0.5276471
## 3 0.001 4 15 500 0.8897511 0.5211765
## 10 0.010 4 5 500 0.9185276 0.7723529
## 11 0.010 4 10 500 0.9165290 0.7705882
## 12 0.010 4 15 500 0.9153323 0.7617647
## 19 0.100 4 5 500 0.9573128 0.8964706
## 20 0.100 4 10 500 0.9550397 0.8994118
## 21 0.100 4 15 500 0.9547391 0.8947059
## 4 0.001 5 5 500 0.8958001 0.5658824
## 5 0.001 5 10 500 0.8958208 0.5658824
## 6 0.001 5 15 500 0.8941702 0.5547059
## 13 0.010 5 5 500 0.9237008 0.7764706
## 14 0.010 5 10 500 0.9228961 0.7741176
## 15 0.010 5 15 500 0.9216394 0.7682353
## 22 0.100 5 5 500 0.9584462 0.8958824
## 23 0.100 5 10 500 0.9581581 0.8976471
## 24 0.100 5 15 500 0.9566970 0.8964706
## 7 0.001 6 5 500 0.9005384 0.5864706
## 8 0.001 6 10 500 0.8994397 0.5870588
## 9 0.001 6 15 500 0.8974198 0.5688235
## 16 0.010 6 5 500 0.9296301 0.7852941
## 17 0.010 6 10 500 0.9281216 0.7794118
## 18 0.010 6 15 500 0.9263270 0.7835294
## 25 0.100 6 5 500 0.9599595 0.8970588
## 26 0.100 6 10 500 0.9594183 0.8964706
## 27 0.100 6 15 500 0.9576763 0.8917647
## Spec ROCSD SensSD SpecSD
## 1 0.9513928 0.02487947 0.05615902 0.01890302
## 2 0.9524424 0.02483276 0.05028752 0.01872530
## 3 0.9545294 0.02523211 0.05077968 0.01875308
## 10 0.9087231 0.02224639 0.05438526 0.02364804
## 11 0.9090740 0.02275322 0.05567557 0.02480784
## 12 0.9104683 0.02329341 0.06048511 0.02549016
## 19 0.9359847 0.01682469 0.05293437 0.02263121
## 20 0.9401892 0.01932518 0.05610765 0.02512296
## 21 0.9450801 0.01725417 0.05141454 0.02269976
## 4 0.9478871 0.02361362 0.03962410 0.02093599
## 5 0.9478932 0.02380831 0.03799889 0.01997374
## 6 0.9517346 0.02417257 0.03948742 0.02023755
## 13 0.9094279 0.02116242 0.05599833 0.02578182
## 14 0.9111701 0.02219566 0.05803392 0.02589530
## 15 0.9115271 0.02228931 0.05441176 0.02431051
## 22 0.9408879 0.01822748 0.05435211 0.02470689
## 23 0.9443753 0.01734441 0.04977603 0.02627205
## 24 0.9436949 0.01742780 0.05361096 0.02330818
## 7 0.9485828 0.02293519 0.03921262 0.01949393
## 8 0.9499832 0.02371313 0.03842337 0.02284164
## 9 0.9496384 0.02343156 0.04043449 0.02088433
## 16 0.9132662 0.02095326 0.05147059 0.02429162
## 17 0.9146606 0.02157192 0.05164536 0.02525835
## 18 0.9164119 0.02092553 0.05494571 0.02154276
## 25 0.9405370 0.01648611 0.05164536 0.02569397
## 26 0.9443905 0.01700477 0.05310432 0.02450729
## 27 0.9471762 0.01647188 0.05248991 0.02258879
(MBS_GBM_Train_AUROC <- MBS_GBM_Tune$results[MBS_GBM_Tune$results$n.trees==MBS_GBM_Tune$bestTune$n.trees &
MBS_GBM_Tune$results$shrinkage==MBS_GBM_Tune$bestTune$shrinkage &
MBS_GBM_Tune$results$n.minobsinnode==MBS_GBM_Tune$bestTune$n.minobsinnode &
MBS_GBM_Tune$results$interaction.depth==MBS_GBM_Tune$bestTune$interaction.depth,
c("ROC")])## [1] 0.9599595
##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_GBM_VarImp <- varImp(MBS_GBM_Tune, scale = TRUE)
plot(MBS_GBM_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariGBMle Importance : Stochastic Gradient Boosting",
xlGBM="Scaled Variable Importance Metrics",
ylGBM="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
MBS_GBM_Test <- data.frame(MBS_GBM_Test_Observed = MA_Test$diagnosis,
MBS_GBM_Test_Predicted = predict(MBS_GBM_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_GBM_Test_ROC <- roc(response = MBS_GBM_Test$MBS_GBM_Test_Observed,
predictor = MBS_GBM_Test$MBS_GBM_Test_Predicted.M,
levels = rev(levels(MBS_GBM_Test$MBS_GBM_Test_Observed)))
(MBS_GBM_Test_AUROC <- auc(MBS_GBM_Test_ROC)[1])## [1] 0.982562
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
XGB_Grid = expand.grid(nrounds = 500,
max_depth = c(4,5,6),
eta = c(0.2,0.3,0.4),
gamma = c(0.1,0.01,0.001),
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1)
##################################
# Running the extreme gradient boosting model
# by setting the caret method to 'xgbTree'
##################################
set.seed(12345678)
MBS_XGB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "xgbTree",
tuneGrid = XGB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBS_XGB_Tune## eXtreme Gradient Boosting
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## eta max_depth gamma ROC Sens Spec
## 0.2 4 0.001 0.9577896 0.8935294 0.9426392
## 0.2 4 0.010 0.9571278 0.8941176 0.9422883
## 0.2 4 0.100 0.9565532 0.8947059 0.9433349
## 0.2 5 0.001 0.9572875 0.8964706 0.9426270
## 0.2 5 0.010 0.9574715 0.8982353 0.9384409
## 0.2 5 0.100 0.9561604 0.8964706 0.9419344
## 0.2 6 0.001 0.9579412 0.8982353 0.9419375
## 0.2 6 0.010 0.9569283 0.8958824 0.9412510
## 0.2 6 0.100 0.9571069 0.8982353 0.9401861
## 0.3 4 0.001 0.9586614 0.8994118 0.9433471
## 0.3 4 0.010 0.9577398 0.8982353 0.9419405
## 0.3 4 0.100 0.9562470 0.8994118 0.9412387
## 0.3 5 0.001 0.9589816 0.8970588 0.9405492
## 0.3 5 0.010 0.9579795 0.8958824 0.9394905
## 0.3 5 0.100 0.9561599 0.8958824 0.9433349
## 0.3 6 0.001 0.9570533 0.8970588 0.9422914
## 0.3 6 0.010 0.9557215 0.8964706 0.9415866
## 0.3 6 0.100 0.9563702 0.8988235 0.9415866
## 0.4 4 0.001 0.9580452 0.8941176 0.9450831
## 0.4 4 0.010 0.9578986 0.8935294 0.9433471
## 0.4 4 0.100 0.9559785 0.8935294 0.9426331
## 0.4 5 0.001 0.9571105 0.8917647 0.9366987
## 0.4 5 0.010 0.9571568 0.8911765 0.9384378
## 0.4 5 0.100 0.9548828 0.8958824 0.9398474
## 0.4 6 0.001 0.9578601 0.8976471 0.9377422
## 0.4 6 0.010 0.9570393 0.9000000 0.9380870
## 0.4 6 0.100 0.9544391 0.8964706 0.9394874
##
## Tuning parameter 'nrounds' was held constant at a value of 500
## Tuning
##
## Tuning parameter 'min_child_weight' was held constant at a value of 1
##
## Tuning parameter 'subsample' was held constant at a value of 1
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were nrounds = 500, max_depth = 5, eta
## = 0.3, gamma = 0.001, colsample_bytree = 1, min_child_weight = 1 and
## subsample = 1.
MBS_XGB_Tune$finalModel## ##### xgb.Booster
## raw: 531.8 Kb
## call:
## xgboost::xgb.train(params = list(eta = param$eta, max_depth = param$max_depth,
## gamma = param$gamma, colsample_bytree = param$colsample_bytree,
## min_child_weight = param$min_child_weight, subsample = param$subsample),
## data = x, nrounds = param$nrounds, objective = "binary:logistic")
## params (as set within xgb.train):
## eta = "0.3", max_depth = "5", gamma = "0.001", colsample_bytree = "1", min_child_weight = "1", subsample = "1", objective = "binary:logistic", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.print.evaluation(period = print_every_n)
## # of features: 6
## niter: 500
## nfeatures : 6
## xNames : texture_mean smoothness_mean compactness_se texture_worst smoothness_worst symmetry_worst
## problemType : Classification
## tuneValue :
## nrounds max_depth eta gamma colsample_bytree min_child_weight subsample
## 13 500 5 0.3 0.001 1 1 1
## obsLevels : M B
## param :
## list()
MBS_XGB_Tune$results## eta max_depth gamma colsample_bytree min_child_weight subsample nrounds
## 1 0.2 4 0.001 1 1 1 500
## 2 0.2 4 0.010 1 1 1 500
## 3 0.2 4 0.100 1 1 1 500
## 10 0.3 4 0.001 1 1 1 500
## 11 0.3 4 0.010 1 1 1 500
## 12 0.3 4 0.100 1 1 1 500
## 19 0.4 4 0.001 1 1 1 500
## 20 0.4 4 0.010 1 1 1 500
## 21 0.4 4 0.100 1 1 1 500
## 4 0.2 5 0.001 1 1 1 500
## 5 0.2 5 0.010 1 1 1 500
## 6 0.2 5 0.100 1 1 1 500
## 13 0.3 5 0.001 1 1 1 500
## 14 0.3 5 0.010 1 1 1 500
## 15 0.3 5 0.100 1 1 1 500
## 22 0.4 5 0.001 1 1 1 500
## 23 0.4 5 0.010 1 1 1 500
## 24 0.4 5 0.100 1 1 1 500
## 7 0.2 6 0.001 1 1 1 500
## 8 0.2 6 0.010 1 1 1 500
## 9 0.2 6 0.100 1 1 1 500
## 16 0.3 6 0.001 1 1 1 500
## 17 0.3 6 0.010 1 1 1 500
## 18 0.3 6 0.100 1 1 1 500
## 25 0.4 6 0.001 1 1 1 500
## 26 0.4 6 0.010 1 1 1 500
## 27 0.4 6 0.100 1 1 1 500
## ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.9577896 0.8935294 0.9426392 0.01687288 0.05221451 0.02278518
## 2 0.9571278 0.8941176 0.9422883 0.01680119 0.05199315 0.02465544
## 3 0.9565532 0.8947059 0.9433349 0.01807507 0.05313825 0.02178664
## 10 0.9586614 0.8994118 0.9433471 0.01770361 0.04644204 0.02428003
## 11 0.9577398 0.8982353 0.9419405 0.01744703 0.05232484 0.02477863
## 12 0.9562470 0.8994118 0.9412387 0.01711876 0.05279801 0.02353039
## 19 0.9580452 0.8941176 0.9450831 0.01815255 0.05233862 0.02081910
## 20 0.9578986 0.8935294 0.9433471 0.01856427 0.04789394 0.02100887
## 21 0.9559785 0.8935294 0.9426331 0.01912049 0.05490634 0.02360832
## 4 0.9572875 0.8964706 0.9426270 0.01809942 0.05327374 0.02092341
## 5 0.9574715 0.8982353 0.9384409 0.01855274 0.05317893 0.01921827
## 6 0.9561604 0.8964706 0.9419344 0.01741132 0.05542902 0.01875888
## 13 0.9589816 0.8970588 0.9405492 0.01887034 0.04987007 0.01807181
## 14 0.9579795 0.8958824 0.9394905 0.01858169 0.05249677 0.02024547
## 15 0.9561599 0.8958824 0.9433349 0.01773607 0.05351675 0.01793777
## 22 0.9571105 0.8917647 0.9366987 0.01917362 0.05231795 0.02404140
## 23 0.9571568 0.8911765 0.9384378 0.01975233 0.05302281 0.02355760
## 24 0.9548828 0.8958824 0.9398474 0.01984338 0.05232484 0.02505943
## 7 0.9579412 0.8982353 0.9419375 0.01852167 0.04706648 0.02263389
## 8 0.9569283 0.8958824 0.9412510 0.01840797 0.04931041 0.02182010
## 9 0.9571069 0.8982353 0.9401861 0.01786090 0.05662560 0.01908666
## 16 0.9570533 0.8970588 0.9422914 0.01803019 0.05129522 0.02147733
## 17 0.9557215 0.8964706 0.9415866 0.01881557 0.04959467 0.02071598
## 18 0.9563702 0.8988235 0.9415866 0.01801038 0.05425255 0.02129219
## 25 0.9578601 0.8976471 0.9377422 0.01829579 0.05377878 0.02572752
## 26 0.9570393 0.9000000 0.9380870 0.01744502 0.05216617 0.02223410
## 27 0.9544391 0.8964706 0.9394874 0.01859835 0.05444488 0.02178696
(MBS_XGB_Train_AUROC <- MBS_XGB_Tune$results[MBS_XGB_Tune$results$nrounds==MBS_XGB_Tune$bestTune$nrounds &
MBS_XGB_Tune$results$max_depth==MBS_XGB_Tune$bestTune$max_depth &
MBS_XGB_Tune$results$eta==MBS_XGB_Tune$bestTune$eta &
MBS_XGB_Tune$results$gamma==MBS_XGB_Tune$bestTune$gamma &
MBS_XGB_Tune$results$colsample_bytree==MBS_XGB_Tune$bestTune$colsample_bytree &
MBS_XGB_Tune$results$min_child_weight==MBS_XGB_Tune$bestTune$min_child_weight &
MBS_XGB_Tune$results$subsample==MBS_XGB_Tune$bestTune$subsample,
c("ROC")])## [1] 0.9589816
##################################
# Identifying and plotting the
# best model predictors
##################################
MBS_XGB_VarImp <- varImp(MBS_XGB_Tune, scale = TRUE)
plot(MBS_XGB_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked VariXGBle Importance : Extreme Gradient Boosting",
xlXGB="Scaled Variable Importance Metrics",
ylXGB="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
MBS_XGB_Test <- data.frame(MBS_XGB_Test_Observed = MA_Test$diagnosis,
MBS_XGB_Test_Predicted = predict(MBS_XGB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBS_XGB_Test_ROC <- roc(response = MBS_XGB_Test$MBS_XGB_Test_Observed,
predictor = MBS_XGB_Test$MBS_XGB_Test_Predicted.M,
levels = rev(levels(MBS_XGB_Test$MBS_XGB_Test_Observed)))
(MBS_XGB_Test_AUROC <- auc(MBS_XGB_Test_ROC)[1])## [1] 0.9830651
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
RF_Grid = data.frame(mtry = c(25,75,125))
##################################
# Running the random forest model
# by setting the caret method to 'rf'
##################################
set.seed(12345678)
MBG_RF_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rf",
tuneGrid = RF_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_RF_Tune## Random Forest
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 25 0.9605251 0.8929412 0.9416018
## 75 0.9609714 0.8970588 0.9436949
## 125 0.9602112 0.8964706 0.9423005
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 75.
MBG_RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 6
##
## OOB estimate of error rate: 4.17%
## Confusion matrix:
## M B class.error
## M 322 18 0.05294118
## B 20 552 0.03496503
MBG_RF_Tune$results## mtry ROC Sens Spec ROCSD SensSD SpecSD
## 1 25 0.9605251 0.8929412 0.9416018 0.01542129 0.05068021 0.02199494
## 2 75 0.9609714 0.8970588 0.9436949 0.01509025 0.05129522 0.02118903
## 3 125 0.9602112 0.8964706 0.9423005 0.01661490 0.05067309 0.02317906
(MBG_RF_Train_AUROC <- MBG_RF_Tune$results[MBG_RF_Tune$results$mtry==MBG_RF_Tune$bestTune$mtry,
c("ROC")])## [1] 0.9609714
##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_RF_VarImp <- varImp(MBG_RF_Tune, scale = TRUE)
plot(MBG_RF_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Random Forest",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
MBG_RF_Test <- data.frame(MBG_RF_Test_Observed = MA_Test$diagnosis,
MBG_RF_Test_Predicted = predict(MBG_RF_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_RF_Test_ROC <- roc(response = MBG_RF_Test$MBG_RF_Test_Observed,
predictor = MBG_RF_Test$MBG_RF_Test_Predicted.M,
levels = rev(levels(MBG_RF_Test$MBG_RF_Test_Observed)))
(MBG_RF_Test_AUROC <- auc(MBG_RF_Test_ROC)[1])## [1] 0.9935446
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the bagged CART model
# by setting the caret method to 'treebag'
##################################
set.seed(12345678)
MBG_BTREE_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "treebag",
nbagg = 50,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
MBG_BTREE_Tune## Bagged CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.957904 0.8976471 0.9457818
MBG_BTREE_Tune$finalModel##
## Bagging classification trees with 50 bootstrap replications
MBG_BTREE_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.957904 0.8976471 0.9457818 0.01659441 0.04793155 0.02103826
(MBG_BTREE_Train_AUROC <- MBG_BTREE_Tune$results$ROC)## [1] 0.957904
##################################
# Identifying and plotting the
# best model predictors
##################################
MBG_BTREE_VarImp <- varImp(MBG_BTREE_Tune, scale = TRUE)
plot(MBG_BTREE_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Bagged Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
MBG_BTREE_Test <- data.frame(MBG_BTREE_Test_Observed = MA_Test$diagnosis,
MBG_BTREE_Test_Predicted = predict(MBG_BTREE_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
##################################
# Reporting the independent evaluation results
# for the test set
##################################
MBG_BTREE_Test_ROC <- roc(response = MBG_BTREE_Test$MBG_BTREE_Test_Observed,
predictor = MBG_BTREE_Test$MBG_BTREE_Test_Predicted.M,
levels = rev(levels(MBG_BTREE_Test$MBG_BTREE_Test_Observed)))
(MBG_BTREE_Test_AUROC <- auc(MBG_BTREE_Test_ROC)[1])## [1] 0.9928739
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# No hyperparameter tuning process required
##################################
# Running the linear discriminant analysis model
# by setting the caret method to 'lda'
##################################
set.seed(12345678)
BAL_LDA_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "lda",
preProc = c("center","scale"),
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_LDA_Tune## Linear Discriminant Analysis
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8736974 0.6988235 0.8831976
BAL_LDA_Tune$finalModel## Call:
## lda(x, y)
##
## Prior probabilities of groups:
## M B
## 0.372807 0.627193
##
## Group means:
## texture_mean smoothness_mean compactness_se texture_worst smoothness_worst
## M 0.5472116 0.4765617 0.4590688 0.5971745 0.5506486
## B -0.3252656 -0.2832710 -0.2728731 -0.3549639 -0.3273086
## symmetry_worst
## M 0.4949332
## B -0.2941911
##
## Coefficients of linear discriminants:
## LD1
## texture_mean -0.5101493
## smoothness_mean -0.2598153
## compactness_se -0.2404049
## texture_worst -0.2745054
## smoothness_worst -0.3118616
## symmetry_worst -0.3480006
BAL_LDA_Tune$results## parameter ROC Sens Spec ROCSD SensSD SpecSD
## 1 none 0.8736974 0.6988235 0.8831976 0.02765351 0.05043067 0.0340081
(BAL_LDA_Train_AUROC <- BAL_LDA_Tune$results$ROC)## [1] 0.8736974
##################################
# Identifying and plotting the
# best model predictors
##################################
BAL_LDA_VarImp <- varImp(BAL_LDA_Tune, scale = TRUE)
plot(BAL_LDA_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Linear Discriminant Analysis",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
BAL_LDA_Test <- data.frame(BAL_LDA_Test_Observed = MA_Test$diagnosis,
BAL_LDA_Test_Predicted = predict(BAL_LDA_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_LDA_Test## BAL_LDA_Test_Observed BAL_LDA_Test_Predicted.M BAL_LDA_Test_Predicted.B
## 8 M 0.847694277 0.15230572
## 13 M 0.752143565 0.24785644
## 16 M 0.984315261 0.01568474
## 19 M 0.648181426 0.35181857
## 24 M 0.622861398 0.37713860
## 31 M 0.917386313 0.08261369
## 37 M 0.793173364 0.20682664
## 42 M 0.907919179 0.09208082
## 44 M 0.764230707 0.23576929
## 46 M 0.728618101 0.27138190
## 48 M 0.859933443 0.14006656
## 54 M 0.491166745 0.50883326
## 61 B 0.156553430 0.84344657
## 65 M 0.944349947 0.05565005
## 69 B 0.781343429 0.21865657
## 70 B 0.048409216 0.95159078
## 71 M 0.248016285 0.75198371
## 72 B 0.071364929 0.92863507
## 82 B 0.539918492 0.46008151
## 89 B 0.527909798 0.47209020
## 93 B 0.007363099 0.99263690
## 98 B 0.155923262 0.84407674
## 103 B 0.143938966 0.85606103
## 104 B 0.481557196 0.51844280
## 107 B 0.675382693 0.32461731
## 109 M 0.958051423 0.04194858
## 116 B 0.481356202 0.51864380
## 123 M 0.935249986 0.06475001
## 125 B 0.021730402 0.97826960
## 129 B 0.262831191 0.73716881
## 130 M 0.836436129 0.16356387
## 136 M 0.544334142 0.45566586
## 139 M 0.567922485 0.43207751
## 147 M 0.805510959 0.19448904
## 149 B 0.119929629 0.88007037
## 150 B 0.035624393 0.96437561
## 162 M 0.048283884 0.95171612
## 167 B 0.003383242 0.99661676
## 185 M 0.494169519 0.50583048
## 191 M 0.984766695 0.01523330
## 194 M 0.965697843 0.03430216
## 197 M 0.923211780 0.07678822
## 200 M 0.829831914 0.17016809
## 201 B 0.397595099 0.60240490
## 209 B 0.832870585 0.16712942
## 211 M 0.490217911 0.50978209
## 213 M 0.051277757 0.94872224
## 217 B 0.578278588 0.42172141
## 221 B 0.028330155 0.97166984
## 222 B 0.127351126 0.87264887
## 223 B 0.321834029 0.67816597
## 228 B 0.080646732 0.91935327
## 237 M 0.878798075 0.12120193
## 239 B 0.386770703 0.61322930
## 242 B 0.017488284 0.98251172
## 246 B 0.572817303 0.42718270
## 256 M 0.450075130 0.54992487
## 259 M 0.910731982 0.08926802
## 262 M 0.300394459 0.69960554
## 263 M 0.556262598 0.44373740
## 266 M 0.914981868 0.08501813
## 272 B 0.037223108 0.96277689
## 274 B 0.115142932 0.88485707
## 275 M 0.477067718 0.52293228
## 285 B 0.017755227 0.98224477
## 300 B 0.221001328 0.77899867
## 308 B 0.009577361 0.99042264
## 328 B 0.017205318 0.98279468
## 345 B 0.152736274 0.84726373
## 349 B 0.125805661 0.87419434
## 356 B 0.100082662 0.89991734
## 363 B 0.279268326 0.72073167
## 365 B 0.067382598 0.93261740
## 368 B 0.289130560 0.71086944
## 382 B 0.059008473 0.94099153
## 383 B 0.099940629 0.90005937
## 387 B 0.029501526 0.97049847
## 388 B 0.011594911 0.98840509
## 401 M 0.916015243 0.08398476
## 403 B 0.121740628 0.87825937
## 417 B 0.688697152 0.31130285
## 420 B 0.438908267 0.56109173
## 428 B 0.538028898 0.46197110
## 434 M 0.754075514 0.24592449
## 442 M 0.720665044 0.27933496
## 444 B 0.060993346 0.93900665
## 445 M 0.127952064 0.87204794
## 454 B 0.063780108 0.93621989
## 455 B 0.116970237 0.88302976
## 460 B 0.398746415 0.60125359
## 462 M 0.848632474 0.15136753
## 463 B 0.214670122 0.78532988
## 472 B 0.424438993 0.57556101
## 484 B 0.099533312 0.90046669
## 489 B 0.266301333 0.73369867
## 493 M 0.569012819 0.43098718
## 494 B 0.003052093 0.99694791
## 497 B 0.533620893 0.46637911
## 498 B 0.182516881 0.81748312
## 501 B 0.056152054 0.94384795
## 502 M 0.965124814 0.03487519
## 507 B 0.469228787 0.53077121
## 509 B 0.047121869 0.95287813
## 525 B 0.140788342 0.85921166
## 526 B 0.147559515 0.85244048
## 527 B 0.638000064 0.36199994
## 531 B 0.288554915 0.71144509
## 532 B 0.651049032 0.34895097
## 534 M 0.415894843 0.58410516
## 537 M 0.705945640 0.29405436
## 544 B 0.493009474 0.50699053
## 546 B 0.442267183 0.55773282
## 548 B 0.210657721 0.78934228
## 550 B 0.511181378 0.48881862
## 551 B 0.067861362 0.93213864
## 556 B 0.672400732 0.32759927
## 557 B 0.194179282 0.80582072
## 575 M 0.804848412 0.19515159
## 578 M 0.961876926 0.03812307
## 581 M 0.641329989 0.35867001
## 583 M 0.428761786 0.57123821
## 589 B 0.116915613 0.88308439
## 590 B 0.225642380 0.77435762
## 601 M 0.892598780 0.10740122
## 603 M 0.904195640 0.09580436
## 611 M 0.907919179 0.09208082
## 617 M 0.859933443 0.14006656
## 619 B 0.375773201 0.62422680
## 625 B 0.221742438 0.77825756
## 628 B 0.043025434 0.95697457
## 632 M 0.860984905 0.13901509
## 646 B 0.055755243 0.94424476
## 649 B 0.248132208 0.75186779
## 657 M 0.747532205 0.25246780
## 662 B 0.007363099 0.99263690
## 665 M 0.729748372 0.27025163
## 677 B 0.169948174 0.83005183
## 679 B 0.579052997 0.42094700
## 685 B 0.481356202 0.51864380
## 687 M 0.797009318 0.20299068
## 689 M 0.379875103 0.62012490
## 695 B 0.052838610 0.94716139
## 701 M 0.524264466 0.47573553
## 704 M 0.632072177 0.36792782
## 706 B 0.051096277 0.94890372
## 709 B 0.055780027 0.94421997
## 715 B 0.209980973 0.79001903
## 726 M 0.588590272 0.41140973
## 734 M 0.556955275 0.44304472
## 747 M 0.650003433 0.34999657
## 752 M 0.696270542 0.30372946
## 763 M 0.965697843 0.03430216
## 765 B 0.068697986 0.93130201
## 775 M 0.266641278 0.73335872
## 780 M 0.490217911 0.50978209
## 786 B 0.578278588 0.42172141
## 792 B 0.321834029 0.67816597
## 796 B 0.081823993 0.91817601
## 809 M 0.971682884 0.02831712
## 813 B 0.213421626 0.78657837
## 816 B 0.079525623 0.92047438
## 818 B 0.817347399 0.18265260
## 820 M 0.681962716 0.31803728
## 823 M 0.336004147 0.66399585
## 850 M 0.944642119 0.05535788
## 854 B 0.017755227 0.98224477
## 865 B 0.021146573 0.97885343
## 867 M 0.051514297 0.94848570
## 870 M 0.652912941 0.34708706
## 876 B 0.033179561 0.96682044
## 882 B 0.029474840 0.97052516
## 886 B 0.004334774 0.99566523
## 895 B 0.157840339 0.84215966
## 896 B 0.013873786 0.98612621
## 905 M 0.647891355 0.35210865
## 906 B 0.032661361 0.96733864
## 913 M 0.809506116 0.19049388
## 917 B 0.063686686 0.93631331
## 919 B 0.196123444 0.80387656
## 922 M 0.720066525 0.27993347
## 923 M 0.853122900 0.14687710
## 925 B 0.100082662 0.89991734
## 928 B 0.027529187 0.97247081
## 932 B 0.279268326 0.72073167
## 936 M 0.883947846 0.11605215
## 941 B 0.010656141 0.98934386
## 950 B 0.303759143 0.69624086
## 953 B 0.403505824 0.59649418
## 956 B 0.029501526 0.97049847
## 967 B 0.026289767 0.97371023
## 973 B 0.164651800 0.83534820
## 974 B 0.011316735 0.98868327
## 976 B 0.059212838 0.94078716
## 980 B 0.367183351 0.63281665
## 985 B 0.640716764 0.35928324
## 987 M 0.778924037 0.22107596
## 993 B 0.238399926 0.76160007
## 1010 B 0.293568883 0.70643112
## 1015 B 0.640576383 0.35942362
## 1023 B 0.063780108 0.93621989
## 1025 B 0.634929643 0.36507036
## 1030 M 0.893535322 0.10646468
## 1034 B 0.062032839 0.93796716
## 1043 B 0.306855621 0.69314438
## 1045 B 0.119438841 0.88056116
## 1046 B 0.281449575 0.71855042
## 1047 B 0.020135949 0.97986405
## 1052 B 0.140996234 0.85900377
## 1060 B 0.423288399 0.57671160
## 1061 B 0.002198833 0.99780117
## 1066 B 0.533620893 0.46637911
## 1068 M 0.279349586 0.72065041
## 1071 M 0.965124814 0.03487519
## 1072 B 0.409662210 0.59033779
## 1077 B 0.473035808 0.52696419
## 1090 B 0.596599276 0.40340072
## 1094 B 0.140788342 0.85921166
## 1097 B 0.034350449 0.96564955
## 1098 B 0.067500501 0.93249950
## 1105 M 0.539179210 0.46082079
## 1106 M 0.705945640 0.29405436
## 1109 B 0.851517966 0.14848203
## 1113 B 0.493009474 0.50699053
## 1129 B 0.516810172 0.48318983
## 1136 M 0.536782216 0.46321778
## 1138 B 0.044202476 0.95579752
##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_LDA_Test_ROC <- roc(response = BAL_LDA_Test$BAL_LDA_Test_Observed,
predictor = BAL_LDA_Test$BAL_LDA_Test_Predicted.M,
levels = rev(levels(BAL_LDA_Test$BAL_LDA_Test_Observed)))
(BAL_LDA_Test_AUROC <- auc(BAL_LDA_Test_ROC)[1])## [1] 0.8984742
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
CART_Grid = data.frame(cp = c(0.001, 0.005, 0.010, 0.015, 0.020))
##################################
# Running the classification and regression tree model
# by setting the caret method to 'rpart'
##################################
set.seed(12345678)
BAL_CART_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "rpart",
tuneGrid = CART_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_CART_Tune## CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## cp ROC Sens Spec
## 0.001 0.8699967 0.7600000 0.8688787
## 0.005 0.8552277 0.7570588 0.8692357
## 0.010 0.8414964 0.7535294 0.8699252
## 0.015 0.8326147 0.7429412 0.8734249
## 0.020 0.8298568 0.7323529 0.8744744
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.001.
BAL_CART_Tune$finalModel## n= 912
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 912 340 B (0.37280702 0.62719298)
## 2) texture_mean>=2.927988 473 196 M (0.58562368 0.41437632)
## 4) smoothness_worst>=-1.52382 252 45 M (0.82142857 0.17857143)
## 8) symmetry_worst>=-1.660064 150 15 M (0.90000000 0.10000000)
## 16) smoothness_worst>=-1.471923 114 5 M (0.95614035 0.04385965) *
## 17) smoothness_worst< -1.471923 36 10 M (0.72222222 0.27777778)
## 34) texture_mean>=3.007903 27 4 M (0.85185185 0.14814815) *
## 35) texture_mean< 3.007903 9 3 B (0.33333333 0.66666667) *
## 9) symmetry_worst< -1.660064 102 30 M (0.70588235 0.29411765)
## 18) texture_mean>=3.079152 51 9 M (0.82352941 0.17647059) *
## 19) texture_mean< 3.079152 51 21 M (0.58823529 0.41176471)
## 38) compactness_se>=-3.816486 28 6 M (0.78571429 0.21428571)
## 76) texture_mean< 3.011847 17 0 M (1.00000000 0.00000000) *
## 77) texture_mean>=3.011847 11 5 B (0.45454545 0.54545455) *
## 39) compactness_se< -3.816486 23 8 B (0.34782609 0.65217391)
## 78) smoothness_mean< -2.312955 10 4 M (0.60000000 0.40000000) *
## 79) smoothness_mean>=-2.312955 13 2 B (0.15384615 0.84615385) *
## 5) smoothness_worst< -1.52382 221 70 B (0.31674208 0.68325792)
## 10) smoothness_mean>=-2.425944 88 43 M (0.51136364 0.48863636)
## 20) texture_worst>=4.527762 58 18 M (0.68965517 0.31034483)
## 40) symmetry_worst>=-1.966444 35 5 M (0.85714286 0.14285714) *
## 41) symmetry_worst< -1.966444 23 10 B (0.43478261 0.56521739)
## 82) texture_worst< 4.804356 13 5 M (0.61538462 0.38461538) *
## 83) texture_worst>=4.804356 10 2 B (0.20000000 0.80000000) *
## 21) texture_worst< 4.527762 30 5 B (0.16666667 0.83333333) *
## 11) smoothness_mean< -2.425944 133 25 B (0.18796992 0.81203008)
## 22) symmetry_worst>=-1.496954 8 2 M (0.75000000 0.25000000) *
## 23) symmetry_worst< -1.496954 125 19 B (0.15200000 0.84800000)
## 46) symmetry_worst>=-1.695215 24 8 B (0.33333333 0.66666667)
## 92) compactness_se< -4.114414 13 5 M (0.61538462 0.38461538) *
## 93) compactness_se>=-4.114414 11 0 B (0.00000000 1.00000000) *
## 47) symmetry_worst< -1.695215 101 11 B (0.10891089 0.89108911)
## 94) compactness_se>=-3.611049 28 8 B (0.28571429 0.71428571)
## 188) compactness_se< -3.197021 9 3 M (0.66666667 0.33333333) *
## 189) compactness_se>=-3.197021 19 2 B (0.10526316 0.89473684) *
## 95) compactness_se< -3.611049 73 3 B (0.04109589 0.95890411) *
## 3) texture_mean< 2.927988 439 63 B (0.14350797 0.85649203)
## 6) symmetry_worst>=-1.325507 25 4 M (0.84000000 0.16000000) *
## 7) symmetry_worst< -1.325507 414 42 B (0.10144928 0.89855072)
## 14) compactness_se>=-3.970723 153 33 B (0.21568627 0.78431373)
## 28) smoothness_worst>=-1.451541 30 13 M (0.56666667 0.43333333)
## 56) symmetry_worst>=-1.619683 14 1 M (0.92857143 0.07142857) *
## 57) symmetry_worst< -1.619683 16 4 B (0.25000000 0.75000000) *
## 29) smoothness_worst< -1.451541 123 16 B (0.13008130 0.86991870)
## 58) compactness_se< -3.427747 70 15 B (0.21428571 0.78571429)
## 116) compactness_se>=-3.705619 28 11 B (0.39285714 0.60714286)
## 232) smoothness_mean>=-2.388266 17 7 M (0.58823529 0.41176471) *
## 233) smoothness_mean< -2.388266 11 1 B (0.09090909 0.90909091) *
## 117) compactness_se< -3.705619 42 4 B (0.09523810 0.90476190) *
## 59) compactness_se>=-3.427747 53 1 B (0.01886792 0.98113208) *
## 15) compactness_se< -3.970723 261 9 B (0.03448276 0.96551724) *
BAL_CART_Tune$results## cp ROC Sens Spec ROCSD SensSD SpecSD
## 1 0.001 0.8699967 0.7600000 0.8688787 0.02958365 0.05611407 0.02331264
## 2 0.005 0.8552277 0.7570588 0.8692357 0.02789001 0.05975368 0.02778803
## 3 0.010 0.8414964 0.7535294 0.8699252 0.02430214 0.05134438 0.02773026
## 4 0.015 0.8326147 0.7429412 0.8734249 0.02618044 0.05304320 0.01922145
## 5 0.020 0.8298568 0.7323529 0.8744744 0.02663895 0.06018642 0.02293389
(BAL_CART_Train_AUROC <- BAL_CART_Tune$results[BAL_CART_Tune$results$cp==BAL_CART_Tune$bestTune$cp,
c("ROC")])## [1] 0.8699967
##################################
# Identifying and plotting the
# best model predictors
##################################
BAL_CART_VarImp <- varImp(BAL_CART_Tune, scale = TRUE)
plot(BAL_CART_VarImp,
top=6,
scales=list(y=list(cex = .95)),
main="Ranked Variable Importance : Classification and Regression Trees",
xlab="Scaled Variable Importance Metrics",
ylab="Predictors",
cex=2,
origin=0,
alpha=0.45)##################################
# Independently evaluating the model
# on the test set
##################################
BAL_CART_Test <- data.frame(BAL_CART_Test_Observed = MA_Test$diagnosis,
BAL_CART_Test_Predicted = predict(BAL_CART_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_CART_Test## BAL_CART_Test_Observed BAL_CART_Test_Predicted.M BAL_CART_Test_Predicted.B
## 8 M 0.95614035 0.04385965
## 13 M 0.85714286 0.14285714
## 16 M 0.95614035 0.04385965
## 19 M 0.82352941 0.17647059
## 24 M 0.82352941 0.17647059
## 31 M 0.95614035 0.04385965
## 37 M 0.95614035 0.04385965
## 42 M 0.15384615 0.84615385
## 44 M 0.95614035 0.04385965
## 46 M 0.84000000 0.16000000
## 48 M 0.84000000 0.16000000
## 54 M 0.16666667 0.83333333
## 61 B 0.03448276 0.96551724
## 65 M 0.95614035 0.04385965
## 69 B 0.84000000 0.16000000
## 70 B 0.03448276 0.96551724
## 71 M 0.85714286 0.14285714
## 72 B 0.01886792 0.98113208
## 82 B 0.92857143 0.07142857
## 89 B 0.00000000 1.00000000
## 93 B 0.03448276 0.96551724
## 98 B 0.15384615 0.84615385
## 103 B 0.04109589 0.95890411
## 104 B 0.15384615 0.84615385
## 107 B 0.25000000 0.75000000
## 109 M 0.95614035 0.04385965
## 116 B 0.45454545 0.54545455
## 123 M 0.95614035 0.04385965
## 125 B 0.01886792 0.98113208
## 129 B 0.01886792 0.98113208
## 130 M 0.85714286 0.14285714
## 136 M 0.82352941 0.17647059
## 139 M 0.01886792 0.98113208
## 147 M 0.84000000 0.16000000
## 149 B 0.09523810 0.90476190
## 150 B 0.03448276 0.96551724
## 162 M 0.09090909 0.90909091
## 167 B 0.03448276 0.96551724
## 185 M 0.85714286 0.14285714
## 191 M 0.95614035 0.04385965
## 194 M 0.95614035 0.04385965
## 197 M 0.95614035 0.04385965
## 200 M 0.95614035 0.04385965
## 201 B 0.60000000 0.40000000
## 209 B 0.85185185 0.14814815
## 211 M 0.85714286 0.14285714
## 213 M 0.58823529 0.41176471
## 217 B 0.01886792 0.98113208
## 221 B 0.03448276 0.96551724
## 222 B 0.09523810 0.90476190
## 223 B 0.03448276 0.96551724
## 228 B 0.09090909 0.90909091
## 237 M 0.95614035 0.04385965
## 239 B 0.66666667 0.33333333
## 242 B 0.03448276 0.96551724
## 246 B 0.15384615 0.84615385
## 256 M 0.92857143 0.07142857
## 259 M 0.95614035 0.04385965
## 262 M 0.04109589 0.95890411
## 263 M 0.85714286 0.14285714
## 266 M 0.82352941 0.17647059
## 272 B 0.03448276 0.96551724
## 274 B 0.03448276 0.96551724
## 275 M 0.82352941 0.17647059
## 285 B 0.01886792 0.98113208
## 300 B 0.16666667 0.83333333
## 308 B 0.03448276 0.96551724
## 328 B 0.03448276 0.96551724
## 345 B 0.03448276 0.96551724
## 349 B 0.03448276 0.96551724
## 356 B 0.10526316 0.89473684
## 363 B 0.85714286 0.14285714
## 365 B 0.03448276 0.96551724
## 368 B 0.03448276 0.96551724
## 382 B 0.09523810 0.90476190
## 383 B 0.10526316 0.89473684
## 387 B 0.09090909 0.90909091
## 388 B 0.03448276 0.96551724
## 401 M 0.95614035 0.04385965
## 403 B 0.01886792 0.98113208
## 417 B 0.15384615 0.84615385
## 420 B 0.85714286 0.14285714
## 428 B 0.00000000 1.00000000
## 434 M 0.85185185 0.14814815
## 442 M 0.82352941 0.17647059
## 444 B 0.03448276 0.96551724
## 445 M 0.03448276 0.96551724
## 454 B 0.03448276 0.96551724
## 455 B 0.03448276 0.96551724
## 460 B 0.04109589 0.95890411
## 462 M 0.82352941 0.17647059
## 463 B 0.04109589 0.95890411
## 472 B 0.04109589 0.95890411
## 484 B 0.03448276 0.96551724
## 489 B 0.03448276 0.96551724
## 493 M 0.85185185 0.14814815
## 494 B 0.03448276 0.96551724
## 497 B 0.92857143 0.07142857
## 498 B 0.03448276 0.96551724
## 501 B 0.03448276 0.96551724
## 502 M 0.95614035 0.04385965
## 507 B 0.15384615 0.84615385
## 509 B 0.03448276 0.96551724
## 525 B 0.09523810 0.90476190
## 526 B 0.03448276 0.96551724
## 527 B 0.95614035 0.04385965
## 531 B 0.03448276 0.96551724
## 532 B 0.95614035 0.04385965
## 534 M 0.85714286 0.14285714
## 537 M 0.82352941 0.17647059
## 544 B 0.04109589 0.95890411
## 546 B 0.85714286 0.14285714
## 548 B 0.09090909 0.90909091
## 550 B 0.00000000 1.00000000
## 551 B 0.04109589 0.95890411
## 556 B 0.82352941 0.17647059
## 557 B 0.16666667 0.83333333
## 575 M 0.84000000 0.16000000
## 578 M 0.95614035 0.04385965
## 581 M 0.84000000 0.16000000
## 583 M 0.66666667 0.33333333
## 589 B 0.03448276 0.96551724
## 590 B 0.09523810 0.90476190
## 601 M 0.95614035 0.04385965
## 603 M 0.95614035 0.04385965
## 611 M 0.15384615 0.84615385
## 617 M 0.84000000 0.16000000
## 619 B 0.04109589 0.95890411
## 625 B 0.16666667 0.83333333
## 628 B 0.04109589 0.95890411
## 632 M 0.82352941 0.17647059
## 646 B 0.58823529 0.41176471
## 649 B 0.03448276 0.96551724
## 657 M 0.85714286 0.14285714
## 662 B 0.03448276 0.96551724
## 665 M 0.85714286 0.14285714
## 677 B 0.03448276 0.96551724
## 679 B 0.60000000 0.40000000
## 685 B 0.45454545 0.54545455
## 687 M 0.92857143 0.07142857
## 689 M 0.75000000 0.25000000
## 695 B 0.03448276 0.96551724
## 701 M 0.15384615 0.84615385
## 704 M 0.95614035 0.04385965
## 706 B 0.03448276 0.96551724
## 709 B 0.01886792 0.98113208
## 715 B 0.01886792 0.98113208
## 726 M 0.45454545 0.54545455
## 734 M 0.75000000 0.25000000
## 747 M 0.33333333 0.66666667
## 752 M 0.85185185 0.14814815
## 763 M 0.95614035 0.04385965
## 765 B 0.03448276 0.96551724
## 775 M 0.92857143 0.07142857
## 780 M 0.85714286 0.14285714
## 786 B 0.01886792 0.98113208
## 792 B 0.03448276 0.96551724
## 796 B 0.03448276 0.96551724
## 809 M 0.82352941 0.17647059
## 813 B 0.04109589 0.95890411
## 816 B 0.03448276 0.96551724
## 818 B 0.95614035 0.04385965
## 820 M 0.85714286 0.14285714
## 823 M 0.03448276 0.96551724
## 850 M 0.95614035 0.04385965
## 854 B 0.01886792 0.98113208
## 865 B 0.03448276 0.96551724
## 867 M 0.03448276 0.96551724
## 870 M 1.00000000 0.00000000
## 876 B 0.03448276 0.96551724
## 882 B 0.09090909 0.90909091
## 886 B 0.03448276 0.96551724
## 895 B 0.03448276 0.96551724
## 896 B 0.03448276 0.96551724
## 905 M 0.15384615 0.84615385
## 906 B 0.09523810 0.90476190
## 913 M 0.85714286 0.14285714
## 917 B 0.03448276 0.96551724
## 919 B 0.09523810 0.90476190
## 922 M 0.84000000 0.16000000
## 923 M 0.82352941 0.17647059
## 925 B 0.10526316 0.89473684
## 928 B 0.09090909 0.90909091
## 932 B 0.85714286 0.14285714
## 936 M 0.85714286 0.14285714
## 941 B 0.03448276 0.96551724
## 950 B 0.03448276 0.96551724
## 953 B 0.01886792 0.98113208
## 956 B 0.09090909 0.90909091
## 967 B 0.01886792 0.98113208
## 973 B 0.03448276 0.96551724
## 974 B 0.03448276 0.96551724
## 976 B 0.03448276 0.96551724
## 980 B 0.03448276 0.96551724
## 985 B 0.85185185 0.14814815
## 987 M 0.95614035 0.04385965
## 993 B 0.85714286 0.14285714
## 1010 B 0.01886792 0.98113208
## 1015 B 0.82352941 0.17647059
## 1023 B 0.03448276 0.96551724
## 1025 B 0.20000000 0.80000000
## 1030 M 0.95614035 0.04385965
## 1034 B 0.03448276 0.96551724
## 1043 B 0.04109589 0.95890411
## 1045 B 0.03448276 0.96551724
## 1046 B 0.61538462 0.38461538
## 1047 B 0.03448276 0.96551724
## 1052 B 0.03448276 0.96551724
## 1060 B 0.61538462 0.38461538
## 1061 B 0.03448276 0.96551724
## 1066 B 0.92857143 0.07142857
## 1068 M 0.58823529 0.41176471
## 1071 M 0.95614035 0.04385965
## 1072 B 0.03448276 0.96551724
## 1077 B 0.25000000 0.75000000
## 1090 B 0.84000000 0.16000000
## 1094 B 0.09523810 0.90476190
## 1097 B 0.03448276 0.96551724
## 1098 B 0.01886792 0.98113208
## 1105 M 0.85714286 0.14285714
## 1106 M 0.82352941 0.17647059
## 1109 B 0.82352941 0.17647059
## 1113 B 0.04109589 0.95890411
## 1129 B 0.20000000 0.80000000
## 1136 M 0.66666667 0.33333333
## 1138 B 0.04109589 0.95890411
##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_CART_Test_ROC <- roc(response = BAL_CART_Test$BAL_CART_Test_Observed,
predictor = BAL_CART_Test$BAL_CART_Test_Predicted.M,
levels = rev(levels(BAL_CART_Test$BAL_CART_Test_Observed)))
(BAL_CART_Test_AUROC <- auc(BAL_CART_Test_ROC)[1])## [1] 0.8843478
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
# used a range of default values
##################################
# Running the support vector machine model
# by setting the caret method to 'svmRadial'
##################################
set.seed(12345678)
BAL_SVM_R_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "svmRadial",
preProc = c("center", "scale"),
tuneLength = 14,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_SVM_R_Tune## Support Vector Machines with Radial Basis Function Kernel
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## C ROC Sens Spec
## 0.25 0.8785695 0.7035294 0.9108375
## 0.50 0.8841244 0.7217647 0.9090831
## 1.00 0.8901422 0.7335294 0.9048848
## 2.00 0.8994429 0.7364706 0.9052326
## 4.00 0.9053698 0.7441176 0.9006834
## 8.00 0.9055011 0.7458824 0.8961373
## 16.00 0.9086274 0.7494118 0.9003295
## 32.00 0.9095067 0.7558824 0.9010191
## 64.00 0.9071082 0.7635294 0.9020809
## 128.00 0.9068724 0.7647059 0.9097574
## 256.00 0.9078494 0.7900000 0.9153623
## 512.00 0.9064750 0.8000000 0.9241190
## 1024.00 0.9055970 0.8076471 0.9262029
## 2048.00 0.9030325 0.8164706 0.9286621
##
## Tuning parameter 'sigma' was held constant at a value of 0.2133227
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.2133227 and C = 32.
BAL_SVM_R_Tune$finalModel## Support Vector Machine object of class "ksvm"
##
## SV type: C-svc (classification)
## parameter : cost C = 32
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 0.213322651914923
##
## Number of Support Vectors : 356
##
## Objective Function Value : -6239.218
## Training error : 0.08114
## Probability model included.
BAL_SVM_R_Tune$results## sigma C ROC Sens Spec ROCSD SensSD
## 1 0.2133227 0.25 0.8785695 0.7035294 0.9108375 0.02404759 0.04721176
## 2 0.2133227 0.50 0.8841244 0.7217647 0.9090831 0.02300940 0.04949281
## 3 0.2133227 1.00 0.8901422 0.7335294 0.9048848 0.02180995 0.04479062
## 4 0.2133227 2.00 0.8994429 0.7364706 0.9052326 0.01913779 0.04531067
## 5 0.2133227 4.00 0.9053698 0.7441176 0.9006834 0.01706242 0.04950738
## 6 0.2133227 8.00 0.9055011 0.7458824 0.8961373 0.01610225 0.05328050
## 7 0.2133227 16.00 0.9086274 0.7494118 0.9003295 0.01630832 0.05259281
## 8 0.2133227 32.00 0.9095067 0.7558824 0.9010191 0.01839949 0.04950738
## 9 0.2133227 64.00 0.9071082 0.7635294 0.9020809 0.01952445 0.05128116
## 10 0.2133227 128.00 0.9068724 0.7647059 0.9097574 0.01936122 0.05469587
## 11 0.2133227 256.00 0.9078494 0.7900000 0.9153623 0.02066747 0.05259966
## 12 0.2133227 512.00 0.9064750 0.8000000 0.9241190 0.02410424 0.04746303
## 13 0.2133227 1024.00 0.9055970 0.8076471 0.9262029 0.02220728 0.05972351
## 14 0.2133227 2048.00 0.9030325 0.8164706 0.9286621 0.02363041 0.05729006
## SpecSD
## 1 0.02975325
## 2 0.03050958
## 3 0.03145146
## 4 0.02573695
## 5 0.02473761
## 6 0.02506890
## 7 0.02219347
## 8 0.02905519
## 9 0.02835256
## 10 0.03106407
## 11 0.02789321
## 12 0.02662871
## 13 0.02696224
## 14 0.02368710
(BAL_SVM_R_Train_AUROC <- BAL_SVM_R_Tune$results[BAL_SVM_R_Tune$results$C==BAL_SVM_R_Tune$bestTune$C,
c("ROC")])## [1] 0.9095067
##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_SVM_R_Test <- data.frame(BAL_SVM_R_Test_Observed = MA_Test$diagnosis,
BAL_SVM_R_Test_Predicted = predict(BAL_SVM_R_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_SVM_R_Test## BAL_SVM_R_Test_Observed BAL_SVM_R_Test_Predicted.M
## 1 M 0.9044222829
## 2 M 0.7290596096
## 3 M 0.9233468355
## 4 M 0.7404179815
## 5 M 0.7945116889
## 6 M 0.9170819657
## 7 M 0.9156025332
## 8 M 0.1669205011
## 9 M 0.9758341031
## 10 M 0.9647140381
## 11 M 0.9932556308
## 12 M 0.4508001411
## 13 B 0.0003314662
## 14 M 0.9021609771
## 15 B 0.2047716701
## 16 B 0.0707212058
## 17 M 0.3236152237
## 18 B 0.0187507336
## 19 B 0.7446307535
## 20 B 0.5427955060
## 21 B 0.0068373114
## 22 B 0.1947931307
## 23 B 0.5253099013
## 24 B 0.5044853061
## 25 B 0.1970896749
## 26 M 0.7689463221
## 27 B 0.7914438541
## 28 M 0.7290744455
## 29 B 0.1631383158
## 30 B 0.0761838734
## 31 M 0.7290996044
## 32 M 0.7349402279
## 33 M 0.7290321252
## 34 M 0.7290625416
## 35 B 0.4316680111
## 36 B 0.0306830496
## 37 M 0.1523193270
## 38 B 0.0087966314
## 39 M 0.7251864555
## 40 M 0.7290782595
## 41 M 0.8598266814
## 42 M 0.7290026567
## 43 M 0.9938849286
## 44 B 0.6063179288
## 45 B 0.8035877278
## 46 M 0.6242223973
## 47 M 0.7290799020
## 48 B 0.4563422587
## 49 B 0.2047403876
## 50 B 0.2047985340
## 51 B 0.0265004852
## 52 B 0.1745819827
## 53 M 0.6781421929
## 54 B 0.0098709363
## 55 B 0.0114137185
## 56 B 0.2264681105
## 57 M 0.4824927787
## 58 M 0.8670805402
## 59 M 0.4355184055
## 60 M 0.7289930039
## 61 M 0.9591627888
## 62 B 0.0052187035
## 63 B 0.1841120773
## 64 M 0.6889729612
## 65 B 0.2138211654
## 66 B 0.0824628982
## 67 B 0.0318241383
## 68 B 0.0083108275
## 69 B 0.0018392697
## 70 B 0.1504943078
## 71 B 0.0405801266
## 72 B 0.2869119087
## 73 B 0.0462521616
## 74 B 0.2216940163
## 75 B 0.0674004043
## 76 B 0.2047863777
## 77 B 0.1316489487
## 78 B 0.0221222591
## 79 M 0.7824602486
## 80 B 0.2048291380
## 81 B 0.5441367870
## 82 B 0.2048147283
## 83 B 0.5165131506
## 84 M 0.9132258558
## 85 M 0.7291484662
## 86 B 0.2048362023
## 87 M 0.1806026856
## 88 B 0.0218300552
## 89 B 0.0270350012
## 90 B 0.0320670836
## 91 M 0.9589181012
## 92 B 0.0169817243
## 93 B 0.0265853072
## 94 B 0.0865747861
## 95 B 0.0084736509
## 96 M 0.6498979581
## 97 B 0.0013661124
## 98 B 0.7197280965
## 99 B 0.1097556022
## 100 B 0.1616817543
## 101 M 0.9315351853
## 102 B 0.3177547602
## 103 B 0.2047586547
## 104 B 0.5262180182
## 105 B 0.1875604282
## 106 B 0.7716527296
## 107 B 0.1445524662
## 108 B 0.8269889251
## 109 M 0.3266559858
## 110 M 0.8787591167
## 111 B 0.1809532465
## 112 B 0.4755611353
## 113 B 0.3888383226
## 114 B 0.2843340190
## 115 B 0.1473702752
## 116 B 0.2047528091
## 117 B 0.1067514334
## 118 M 0.9479107742
## 119 M 0.9773435443
## 120 M 0.5754594849
## 121 M 0.6015802796
## 122 B 0.0649192935
## 123 B 0.0213288903
## 124 M 0.9742491322
## 125 M 0.7290773094
## 126 M 0.1669205011
## 127 M 0.9932556308
## 128 B 0.4614325728
## 129 B 0.0275797282
## 130 B 0.0522901184
## 131 M 0.7290866363
## 132 B 0.2047713327
## 133 B 0.1584687810
## 134 M 0.8507838033
## 135 B 0.0068373114
## 136 M 0.7290163932
## 137 B 0.2047251065
## 138 B 0.2047885799
## 139 B 0.7914438541
## 140 M 0.9674604705
## 141 M 0.7289655089
## 142 B 0.0814530866
## 143 M 0.1674733888
## 144 M 0.7633769432
## 145 B 0.2047902442
## 146 B 0.2047931519
## 147 B 0.1856451887
## 148 M 0.6585283243
## 149 M 0.7291168244
## 150 M 0.7704623651
## 151 M 0.8547308285
## 152 M 0.8598266814
## 153 B 0.0993871425
## 154 M 0.5397197215
## 155 M 0.6242223973
## 156 B 0.4563422587
## 157 B 0.0265004852
## 158 B 0.0059930669
## 159 M 0.7890539791
## 160 B 0.1823812336
## 161 B 0.0763185137
## 162 B 0.6154978701
## 163 M 0.8133833089
## 164 M 0.1518747402
## 165 M 0.7995780848
## 166 B 0.2138211654
## 167 B 0.0007057461
## 168 M 0.5019879468
## 169 M 0.7290765956
## 170 B 0.0151907106
## 171 B 0.2047602499
## 172 B 0.0018950016
## 173 B 0.0297726297
## 174 B 0.0346484595
## 175 M 0.3196586472
## 176 B 0.2047513452
## 177 M 0.7719056636
## 178 B 0.0245628151
## 179 B 0.1397775263
## 180 M 0.9219526232
## 181 M 0.7290235658
## 182 B 0.0405801266
## 183 B 0.0881705707
## 184 B 0.2869119087
## 185 M 0.7691243509
## 186 B 0.0016429278
## 187 B 0.2047986697
## 188 B 0.2910240994
## 189 B 0.1316489487
## 190 B 0.1988594824
## 191 B 0.0054014539
## 192 B 0.0026813409
## 193 B 0.0354992607
## 194 B 0.2047453695
## 195 B 0.8763465750
## 196 M 0.9189588302
## 197 B 0.1960193136
## 198 B 0.0928743590
## 199 B 0.6866198710
## 200 B 0.0218300552
## 201 B 0.2047956979
## 202 M 0.7291675790
## 203 B 0.0329259941
## 204 B 0.0133748316
## 205 B 0.1984969871
## 206 B 0.2047046925
## 207 B 0.0555626842
## 208 B 0.0041192726
## 209 B 0.4214333492
## 210 B 0.0239206631
## 211 B 0.7197280965
## 212 M 0.1521024957
## 213 M 0.9315351853
## 214 B 0.0344278876
## 215 B 0.1387945330
## 216 B 0.2047557364
## 217 B 0.5262180182
## 218 B 0.0012688405
## 219 B 0.1231909083
## 220 M 0.5289037277
## 221 M 0.8787591167
## 222 B 0.2048531346
## 223 B 0.1809532465
## 224 B 0.2048034409
## 225 M 0.3484941721
## 226 B 0.2048194280
## BAL_SVM_R_Test_Predicted.B
## 1 0.095577717
## 2 0.270940390
## 3 0.076653165
## 4 0.259582018
## 5 0.205488311
## 6 0.082918034
## 7 0.084397467
## 8 0.833079499
## 9 0.024165897
## 10 0.035285962
## 11 0.006744369
## 12 0.549199859
## 13 0.999668534
## 14 0.097839023
## 15 0.795228330
## 16 0.929278794
## 17 0.676384776
## 18 0.981249266
## 19 0.255369247
## 20 0.457204494
## 21 0.993162689
## 22 0.805206869
## 23 0.474690099
## 24 0.495514694
## 25 0.802910325
## 26 0.231053678
## 27 0.208556146
## 28 0.270925554
## 29 0.836861684
## 30 0.923816127
## 31 0.270900396
## 32 0.265059772
## 33 0.270967875
## 34 0.270937458
## 35 0.568331989
## 36 0.969316950
## 37 0.847680673
## 38 0.991203369
## 39 0.274813545
## 40 0.270921741
## 41 0.140173319
## 42 0.270997343
## 43 0.006115071
## 44 0.393682071
## 45 0.196412272
## 46 0.375777603
## 47 0.270920098
## 48 0.543657741
## 49 0.795259612
## 50 0.795201466
## 51 0.973499515
## 52 0.825418017
## 53 0.321857807
## 54 0.990129064
## 55 0.988586282
## 56 0.773531890
## 57 0.517507221
## 58 0.132919460
## 59 0.564481595
## 60 0.271006996
## 61 0.040837211
## 62 0.994781297
## 63 0.815887923
## 64 0.311027039
## 65 0.786178835
## 66 0.917537102
## 67 0.968175862
## 68 0.991689173
## 69 0.998160730
## 70 0.849505692
## 71 0.959419873
## 72 0.713088091
## 73 0.953747838
## 74 0.778305984
## 75 0.932599596
## 76 0.795213622
## 77 0.868351051
## 78 0.977877741
## 79 0.217539751
## 80 0.795170862
## 81 0.455863213
## 82 0.795185272
## 83 0.483486849
## 84 0.086774144
## 85 0.270851534
## 86 0.795163798
## 87 0.819397314
## 88 0.978169945
## 89 0.972964999
## 90 0.967932916
## 91 0.041081899
## 92 0.983018276
## 93 0.973414693
## 94 0.913425214
## 95 0.991526349
## 96 0.350102042
## 97 0.998633888
## 98 0.280271903
## 99 0.890244398
## 100 0.838318246
## 101 0.068464815
## 102 0.682245240
## 103 0.795241345
## 104 0.473781982
## 105 0.812439572
## 106 0.228347270
## 107 0.855447534
## 108 0.173011075
## 109 0.673344014
## 110 0.121240883
## 111 0.819046753
## 112 0.524438865
## 113 0.611161677
## 114 0.715665981
## 115 0.852629725
## 116 0.795247191
## 117 0.893248567
## 118 0.052089226
## 119 0.022656456
## 120 0.424540515
## 121 0.398419720
## 122 0.935080706
## 123 0.978671110
## 124 0.025750868
## 125 0.270922691
## 126 0.833079499
## 127 0.006744369
## 128 0.538567427
## 129 0.972420272
## 130 0.947709882
## 131 0.270913364
## 132 0.795228667
## 133 0.841531219
## 134 0.149216197
## 135 0.993162689
## 136 0.270983607
## 137 0.795274894
## 138 0.795211420
## 139 0.208556146
## 140 0.032539529
## 141 0.271034491
## 142 0.918546913
## 143 0.832526611
## 144 0.236623057
## 145 0.795209756
## 146 0.795206848
## 147 0.814354811
## 148 0.341471676
## 149 0.270883176
## 150 0.229537635
## 151 0.145269171
## 152 0.140173319
## 153 0.900612857
## 154 0.460280278
## 155 0.375777603
## 156 0.543657741
## 157 0.973499515
## 158 0.994006933
## 159 0.210946021
## 160 0.817618766
## 161 0.923681486
## 162 0.384502130
## 163 0.186616691
## 164 0.848125260
## 165 0.200421915
## 166 0.786178835
## 167 0.999294254
## 168 0.498012053
## 169 0.270923404
## 170 0.984809289
## 171 0.795239750
## 172 0.998104998
## 173 0.970227370
## 174 0.965351540
## 175 0.680341353
## 176 0.795248655
## 177 0.228094336
## 178 0.975437185
## 179 0.860222474
## 180 0.078047377
## 181 0.270976434
## 182 0.959419873
## 183 0.911829429
## 184 0.713088091
## 185 0.230875649
## 186 0.998357072
## 187 0.795201330
## 188 0.708975901
## 189 0.868351051
## 190 0.801140518
## 191 0.994598546
## 192 0.997318659
## 193 0.964500739
## 194 0.795254631
## 195 0.123653425
## 196 0.081041170
## 197 0.803980686
## 198 0.907125641
## 199 0.313380129
## 200 0.978169945
## 201 0.795204302
## 202 0.270832421
## 203 0.967074006
## 204 0.986625168
## 205 0.801503013
## 206 0.795295308
## 207 0.944437316
## 208 0.995880727
## 209 0.578566651
## 210 0.976079337
## 211 0.280271903
## 212 0.847897504
## 213 0.068464815
## 214 0.965572112
## 215 0.861205467
## 216 0.795244264
## 217 0.473781982
## 218 0.998731159
## 219 0.876809092
## 220 0.471096272
## 221 0.121240883
## 222 0.795146865
## 223 0.819046753
## 224 0.795196559
## 225 0.651505828
## 226 0.795180572
##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_SVM_R_Test_ROC <- roc(response = BAL_SVM_R_Test$BAL_SVM_R_Test_Observed,
predictor = BAL_SVM_R_Test$BAL_SVM_R_Test_Predicted.M,
levels = rev(levels(BAL_SVM_R_Test$BAL_SVM_R_Test_Observed)))
(BAL_SVM_R_Test_AUROC <- auc(BAL_SVM_R_Test_ROC)[1])## [1] 0.9159121
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
KNN_Grid = data.frame(k = 1:15)
##################################
# Running the k-nearest neighbors model
# by setting the caret method to 'knn'
##################################
set.seed(12345678)
BAL_KNN_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "knn",
preProc = c("center", "scale"),
tuneGrid = KNN_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_KNN_Tune## k-Nearest Neighbors
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## k ROC Sens Spec
## 1 0.8999215 0.8841176 0.9157254
## 2 0.8810826 0.7476471 0.8335652
## 3 0.8881622 0.7223529 0.8440793
## 4 0.8843389 0.7264706 0.8751670
## 5 0.8801288 0.7294118 0.8877712
## 6 0.8822927 0.7111765 0.8800671
## 7 0.8826117 0.7105882 0.8828680
## 8 0.8824661 0.7176471 0.8814737
## 9 0.8822659 0.7141176 0.8811106
## 10 0.8835703 0.6958824 0.8779588
## 11 0.8866311 0.7100000 0.8793593
## 12 0.8871688 0.7094118 0.8790175
## 13 0.8888748 0.7094118 0.8776171
## 14 0.8887794 0.7105882 0.8797437
## 15 0.8901884 0.7082353 0.8793715
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was k = 1.
BAL_KNN_Tune$finalModel## 1-nearest neighbor model
## Training set outcome distribution:
##
## M B
## 340 572
BAL_KNN_Tune$results## k ROC Sens Spec ROCSD SensSD SpecSD
## 1 1 0.8999215 0.8841176 0.9157254 0.02896451 0.05993436 0.02526390
## 2 2 0.8810826 0.7476471 0.8335652 0.02389197 0.06191043 0.03572945
## 3 3 0.8881622 0.7223529 0.8440793 0.02482202 0.04827623 0.02869459
## 4 4 0.8843389 0.7264706 0.8751670 0.02288013 0.05453087 0.02753525
## 5 5 0.8801288 0.7294118 0.8877712 0.02422588 0.05076548 0.03244839
## 6 6 0.8822927 0.7111765 0.8800671 0.02770719 0.05434548 0.03295881
## 7 7 0.8826117 0.7105882 0.8828680 0.02576758 0.05497850 0.03623026
## 8 8 0.8824661 0.7176471 0.8814737 0.02787252 0.04611492 0.03152335
## 9 9 0.8822659 0.7141176 0.8811106 0.02617793 0.04494326 0.03184781
## 10 10 0.8835703 0.6958824 0.8779588 0.02477549 0.04131629 0.03514350
## 11 11 0.8866311 0.7100000 0.8793593 0.02563095 0.04213691 0.03419137
## 12 12 0.8871688 0.7094118 0.8790175 0.02675550 0.03707710 0.03037541
## 13 13 0.8888748 0.7094118 0.8776171 0.02545936 0.03756003 0.03167993
## 14 14 0.8887794 0.7105882 0.8797437 0.02563345 0.03976031 0.03542890
## 15 15 0.8901884 0.7082353 0.8793715 0.02412346 0.04259632 0.03291041
(BAL_KNN_Train_AUROC <- BAL_KNN_Tune$results[BAL_KNN_Tune$results$k==BAL_KNN_Tune$bestTune$k,
c("ROC")])## [1] 0.8999215
##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_KNN_Test <- data.frame(BAL_KNN_Test_Observed = MA_Test$diagnosis,
BAL_KNN_Test_Predicted = predict(BAL_KNN_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_KNN_Test## BAL_KNN_Test_Observed BAL_KNN_Test_Predicted.M BAL_KNN_Test_Predicted.B
## 1 M 1 0
## 2 M 1 0
## 3 M 1 0
## 4 M 1 0
## 5 M 1 0
## 6 M 1 0
## 7 M 1 0
## 8 M 1 0
## 9 M 1 0
## 10 M 1 0
## 11 M 1 0
## 12 M 1 0
## 13 B 0 1
## 14 M 1 0
## 15 B 0 1
## 16 B 0 1
## 17 M 1 0
## 18 B 0 1
## 19 B 0 1
## 20 B 0 1
## 21 B 0 1
## 22 B 0 1
## 23 B 0 1
## 24 B 0 1
## 25 B 0 1
## 26 M 1 0
## 27 B 1 0
## 28 M 1 0
## 29 B 0 1
## 30 B 0 1
## 31 M 1 0
## 32 M 1 0
## 33 M 1 0
## 34 M 1 0
## 35 B 0 1
## 36 B 0 1
## 37 M 1 0
## 38 B 0 1
## 39 M 1 0
## 40 M 1 0
## 41 M 1 0
## 42 M 1 0
## 43 M 1 0
## 44 B 0 1
## 45 B 0 1
## 46 M 1 0
## 47 M 1 0
## 48 B 1 0
## 49 B 0 1
## 50 B 0 1
## 51 B 0 1
## 52 B 0 1
## 53 M 1 0
## 54 B 0 1
## 55 B 0 1
## 56 B 0 1
## 57 M 1 0
## 58 M 1 0
## 59 M 1 0
## 60 M 1 0
## 61 M 1 0
## 62 B 0 1
## 63 B 0 1
## 64 M 1 0
## 65 B 0 1
## 66 B 0 1
## 67 B 0 1
## 68 B 0 1
## 69 B 0 1
## 70 B 0 1
## 71 B 0 1
## 72 B 0 1
## 73 B 0 1
## 74 B 0 1
## 75 B 0 1
## 76 B 0 1
## 77 B 0 1
## 78 B 0 1
## 79 M 1 0
## 80 B 0 1
## 81 B 0 1
## 82 B 0 1
## 83 B 0 1
## 84 M 1 0
## 85 M 1 0
## 86 B 0 1
## 87 M 1 0
## 88 B 0 1
## 89 B 0 1
## 90 B 0 1
## 91 M 1 0
## 92 B 0 1
## 93 B 0 1
## 94 B 0 1
## 95 B 0 1
## 96 M 1 0
## 97 B 0 1
## 98 B 1 0
## 99 B 0 1
## 100 B 0 1
## 101 M 1 0
## 102 B 0 1
## 103 B 0 1
## 104 B 1 0
## 105 B 0 1
## 106 B 0 1
## 107 B 0 1
## 108 B 0 1
## 109 M 1 0
## 110 M 1 0
## 111 B 0 1
## 112 B 0 1
## 113 B 0 1
## 114 B 0 1
## 115 B 0 1
## 116 B 0 1
## 117 B 0 1
## 118 M 1 0
## 119 M 1 0
## 120 M 1 0
## 121 M 1 0
## 122 B 0 1
## 123 B 0 1
## 124 M 1 0
## 125 M 1 0
## 126 M 1 0
## 127 M 1 0
## 128 B 0 1
## 129 B 0 1
## 130 B 0 1
## 131 M 1 0
## 132 B 0 1
## 133 B 0 1
## 134 M 1 0
## 135 B 0 1
## 136 M 1 0
## 137 B 0 1
## 138 B 0 1
## 139 B 1 0
## 140 M 1 0
## 141 M 1 0
## 142 B 0 1
## 143 M 1 0
## 144 M 1 0
## 145 B 0 1
## 146 B 0 1
## 147 B 0 1
## 148 M 1 0
## 149 M 1 0
## 150 M 1 0
## 151 M 1 0
## 152 M 1 0
## 153 B 0 1
## 154 M 1 0
## 155 M 1 0
## 156 B 1 0
## 157 B 0 1
## 158 B 0 1
## 159 M 1 0
## 160 B 0 1
## 161 B 0 1
## 162 B 0 1
## 163 M 1 0
## 164 M 1 0
## 165 M 1 0
## 166 B 0 1
## 167 B 0 1
## 168 M 1 0
## 169 M 1 0
## 170 B 0 1
## 171 B 0 1
## 172 B 0 1
## 173 B 0 1
## 174 B 0 1
## 175 M 1 0
## 176 B 0 1
## 177 M 1 0
## 178 B 0 1
## 179 B 0 1
## 180 M 1 0
## 181 M 1 0
## 182 B 0 1
## 183 B 0 1
## 184 B 0 1
## 185 M 1 0
## 186 B 0 1
## 187 B 0 1
## 188 B 0 1
## 189 B 0 1
## 190 B 0 1
## 191 B 0 1
## 192 B 0 1
## 193 B 0 1
## 194 B 0 1
## 195 B 0 1
## 196 M 1 0
## 197 B 0 1
## 198 B 0 1
## 199 B 0 1
## 200 B 0 1
## 201 B 0 1
## 202 M 1 0
## 203 B 0 1
## 204 B 0 1
## 205 B 0 1
## 206 B 0 1
## 207 B 0 1
## 208 B 0 1
## 209 B 0 1
## 210 B 0 1
## 211 B 1 0
## 212 M 1 0
## 213 M 1 0
## 214 B 0 1
## 215 B 0 1
## 216 B 0 1
## 217 B 1 0
## 218 B 0 1
## 219 B 0 1
## 220 M 1 0
## 221 M 1 0
## 222 B 0 1
## 223 B 0 1
## 224 B 0 1
## 225 M 1 0
## 226 B 0 1
##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_KNN_Test_ROC <- roc(response = BAL_KNN_Test$BAL_KNN_Test_Observed,
predictor = BAL_KNN_Test$BAL_KNN_Test_Predicted.M,
levels = rev(levels(BAL_KNN_Test$BAL_KNN_Test_Observed)))
(BAL_KNN_Test_AUROC <- auc(BAL_KNN_Test_ROC)[1])## [1] 0.971831
##################################
# Setting the conditions
# for hyperparameter tuning
##################################
NB_Grid = data.frame(usekernel = c(TRUE, FALSE),
fL = 2,
adjust = FALSE)
##################################
# Running the naive bayes model
# by setting the caret method to 'nb'
##################################
set.seed(12345678)
BAL_NB_Tune <- train(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
method = "nb",
tuneGrid = NB_Grid,
metric = "ROC",
trControl = RKFold_Control)
##################################
# Reporting the cross-validation results
# for the train set
##################################
BAL_NB_Tune## Naive Bayes
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 729, 729, 730, 730, 730, 730, ...
## Resampling results across tuning parameters:
##
## usekernel ROC Sens Spec
## FALSE 0.8864212 0.7576471 0.8643356
## TRUE NaN NaN NaN
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were fL = 2, usekernel = FALSE and adjust
## = FALSE.
BAL_NB_Tune$finalModel## $apriori
## grouping
## M B
## 0.372807 0.627193
##
## $tables
## $tables$texture_mean
## [,1] [,2]
## M 3.053171 0.1768317
## B 2.861218 0.2118987
##
## $tables$smoothness_mean
## [,1] [,2]
## M -2.283184 0.1220104
## B -2.392952 0.1412754
##
## $tables$compactness_se
## [,1] [,2]
## M -3.579629 0.5293692
## B -4.063368 0.6656621
##
## $tables$texture_worst
## [,1] [,2]
## M 4.790170 0.3648083
## B 4.360104 0.4212065
##
## $tables$smoothness_worst
## [,1] [,2]
## M -1.471766 0.08336974
## B -1.553719 0.08524520
##
## $tables$symmetry_worst
## [,1] [,2]
## M -1.588074 0.4012040
## B -1.881098 0.3039037
##
##
## $levels
## [1] "M" "B"
##
## $call
## NaiveBayes.default(x = x, grouping = y, usekernel = FALSE, fL = param$fL)
##
## $x
## texture_mean smoothness_mean compactness_se texture_worst
## X1 2.339881 -2.133687 -3.015119 3.845649
## X2 2.877512 -2.468168 -4.336671 4.393994
## X3 3.056357 -2.210918 -3.217377 4.558289
## X4 3.014554 -1.948413 -2.595883 4.629842
## X5 2.663053 -2.299590 -3.704602 3.777223
## X6 2.753661 -2.057289 -3.397703 4.421124
## X7 2.994732 -2.357781 -4.281638 4.712710
## X9 3.082827 -2.061209 -3.351836 4.919334
## X10 3.179719 -2.131999 -2.628731 5.491708
## X11 3.145875 -2.500305 -4.681080 5.114832
## X12 2.884242 -2.332014 -3.203741 4.685875
## X14 3.175968 -2.476819 -3.465416 4.712710
## X15 3.118392 -2.179483 -2.824135 5.000625
## X17 3.002211 -2.315974 -4.455028 4.928999
## X18 3.029167 -2.145581 -3.688480 4.967287
## X20 2.664447 -2.324933 -4.226734 4.034440
## X21 2.754297 -2.230264 -3.964369 4.146994
## X22 2.520917 -2.278869 -4.246098 3.668189
## X23 2.657458 -2.232127 -2.932194 4.017490
## X25 3.062456 -2.188364 -3.972835 4.972347
## X26 2.797281 -2.131999 -3.270432 4.226835
## X27 3.069447 -2.249993 -3.488391 5.074506
## X28 3.008155 -2.360214 -3.603803 4.684455
## X29 3.229618 -2.223774 -3.487736 5.278432
## X30 2.711378 -2.318003 -3.495618 4.058702
## X32 2.928524 -2.199126 -3.377286 4.744803
## X33 3.177220 -2.122767 -3.479591 5.005619
## X34 3.276012 -2.364354 -3.405808 4.930285
## X35 2.883683 -2.263364 -3.551555 4.684455
## X36 3.072230 -2.342366 -3.689280 4.806397
## X38 2.913437 -2.409836 -5.318724 4.345339
## X39 3.226844 -2.365844 -4.515329 4.533450
## X40 3.035914 -2.286712 -3.799141 4.594701
## X41 3.071767 -2.505681 -4.508043 4.888151
## X43 3.211247 -2.398986 -2.296603 5.072078
## X45 3.082369 -2.331602 -4.280192 4.864503
## X47 2.823757 -2.453408 -4.106822 4.274627
## X49 2.683074 -2.272056 -4.249596 4.165667
## X50 3.104587 -2.435888 -4.281638 4.988725
## X51 3.072693 -2.449115 -4.629668 4.572474
## X52 2.793616 -2.565900 -4.442201 4.376271
## X53 2.903617 -2.493625 -4.781907 4.220791
## X55 3.091951 -2.401743 -4.575611 4.980549
## X56 2.931194 -2.351355 -4.741907 4.317312
## X57 2.921547 -2.250942 -3.769656 4.746189
## X58 3.072230 -2.174192 -3.556098 4.917397
## X59 2.960623 -2.518257 -4.756807 4.298995
## X60 2.467252 -2.327698 -4.551629 3.639212
## X62 3.043570 -2.085057 -3.453965 4.668773
## X63 3.097837 -2.254748 -2.651292 4.839292
## X64 2.629007 -2.561226 -3.234497 4.031624
## X66 3.175551 -2.143873 -3.767923 5.085404
## X67 3.044999 -2.259526 -4.042701 4.972347
## X68 2.946542 -2.508503 -4.686814 4.428254
## X73 3.199489 -2.233992 -2.879551 5.111247
## X74 2.759377 -2.295609 -3.880040 4.179793
## X75 2.804572 -2.389015 -4.006883 4.377888
## X76 2.978077 -2.389451 -3.815350 4.484527
## X77 2.392426 -2.047168 -3.561718 3.284809
## X78 2.781920 -2.239610 -2.840611 4.001364
## X79 3.176803 -2.051048 -2.683114 4.982438
## X80 2.890372 -2.309207 -4.097750 4.504524
## X81 3.043093 -2.205458 -4.071019 5.009980
## X83 3.215269 -2.241490 -2.865933 5.099260
## X84 3.269189 -2.107841 -2.805112 5.044600
## X85 2.750471 -2.330676 -4.010739 4.510643
## X86 2.918851 -2.315265 -4.105001 4.714115
## X87 3.066191 -2.359791 -3.512241 4.821893
## X88 3.202340 -2.404729 -3.994318 4.898589
## X90 2.723924 -2.178599 -3.120842 3.936655
## X91 3.178887 -2.410839 -4.007433 4.812472
## X92 3.125005 -2.385967 -3.711534 4.581390
## X94 2.906901 -2.280824 -4.205723 4.588794
## X95 2.987196 -2.264326 -3.292792 4.458901
## X96 3.136798 -2.399316 -3.357563 4.974243
## X97 2.881443 -2.258568 -4.440504 4.185067
## X99 2.552565 -2.409836 -4.319991 3.828226
## X100 2.984166 -2.327698 -3.542185 4.927712
## X101 3.218076 -2.355142 -4.207737 5.196499
## X102 2.597491 -2.145581 -4.524512 4.060557
## X105 2.959587 -2.303686 -3.808114 4.385955
## X106 2.744704 -1.967542 -3.536330 4.311499
## X108 2.919931 -2.467814 -4.559241 4.700742
## X110 3.056827 -2.435088 -4.162409 4.815168
## X111 2.832625 -2.266253 -3.529485 4.232863
## X112 3.033028 -2.309308 -3.208431 4.553792
## X113 2.978077 -2.546314 -2.597493 4.419537
## X114 3.005187 -2.187472 -3.284215 4.340417
## X115 2.761907 -2.162823 -3.810821 4.067964
## X117 2.757475 -2.357886 -2.694147 3.818947
## X118 2.813611 -2.152442 -3.663992 4.692258
## X119 3.131573 -2.158485 -3.224894 4.904441
## X120 2.996232 -2.476700 -4.776908 4.724620
## X121 2.381396 -2.367337 -4.180556 3.702239
## X122 2.840247 -2.249993 -3.862757 4.510643
## X124 2.387845 -2.206366 -4.361440 3.703328
## X126 2.845491 -2.432124 -4.691927 4.407598
## X127 3.206398 -2.379682 -4.444753 5.217803
## X128 2.939691 -2.498965 -3.600502 4.573218
## X131 2.587012 -2.238672 -3.706636 3.894116
## X132 2.969388 -2.214574 -4.210429 4.593226
## X133 3.069912 -2.294617 -3.936316 4.979920
## X134 2.634045 -2.357886 -4.189755 4.033502
## X135 3.086943 -2.361274 -4.253106 4.961581
## X137 2.813611 -2.252843 -4.283087 4.554542
## X138 2.733718 -2.339353 -4.185802 4.279690
## X140 2.594508 -2.150723 -3.352979 3.680332
## X141 2.482404 -2.380547 -5.175038 3.488165
## X142 2.893146 -2.330882 -3.943514 4.538741
## X143 2.851284 -2.214574 -4.054163 4.648665
## X144 2.767576 -2.444494 -4.158563 4.262772
## X145 2.706048 -2.551944 -4.027995 4.167437
## X146 2.684440 -2.161086 -3.048922 3.760309
## X148 2.932260 -2.508626 -3.020640 4.553792
## X151 3.033991 -2.175952 -4.472389 4.449513
## X152 3.030134 -2.363929 -2.915813 4.853256
## X153 2.730464 -2.233059 -2.344866 4.055916
## X154 2.571084 -2.327493 -4.712199 3.737909
## X155 2.730464 -2.366164 -3.903559 4.147887
## X156 2.887033 -2.447149 -4.159203 4.534963
## X157 3.032064 -2.193731 -3.004975 4.526631
## X158 2.968361 -2.597628 -3.626468 4.741335
## X159 2.544747 -2.373974 -4.626496 3.953251
## X160 2.561868 -2.588269 -5.093908 3.932732
## X161 3.004692 -2.217325 -3.727620 4.608673
## X163 2.898671 -2.189256 -3.782311 4.621834
## X164 3.100993 -2.290657 -3.449863 4.783310
## X165 3.092859 -2.472306 -3.671433 4.751724
## X166 2.983660 -2.474442 -4.830441 4.579906
## X168 2.933857 -2.423059 -3.700952 4.615263
## X169 3.205993 -2.254748 -3.314836 5.020540
## X170 2.830268 -2.317191 -4.290359 4.360856
## X171 2.516890 -2.274970 -4.439656 3.665973
## X172 2.977059 -2.402626 -4.544075 4.863182
## X173 2.475698 -2.073857 -3.763172 3.815845
## X174 2.688528 -2.296603 -3.853283 3.792962
## X175 2.718001 -2.431328 -4.588313 4.028805
## X176 2.670694 -2.392729 -4.827439 3.815845
## X177 2.893700 -2.333147 -2.429510 4.471360
## X178 3.001217 -2.319630 -3.195648 4.767568
## X179 3.100993 -2.772429 -6.095937 4.806397
## X180 2.569554 -2.437374 -5.057098 3.721768
## X181 3.085116 -2.212744 -3.674188 5.052569
## X182 3.279783 -2.170680 -3.045133 5.090835
## X183 3.011113 -2.343720 -3.788479 5.050733
## X184 2.702703 -2.401411 -3.289298 3.883102
## X186 2.715357 -2.378710 -4.422849 4.207786
## X187 2.922086 -2.454804 -4.690619 4.619646
## X188 2.844328 -2.325444 -4.743973 4.225973
## X189 2.855895 -2.295609 -4.735735 4.628388
## X190 2.766319 -2.515778 -3.811273 4.065190
## X192 3.063858 -2.436231 -4.022955 4.401206
## X193 2.902520 -2.666429 -5.000289 4.177151
## X195 3.144583 -2.259526 -2.971625 4.721123
## X196 2.793004 -2.533131 -4.143325 4.278004
## X198 3.083743 -2.607617 -2.938218 4.495315
## X199 3.113071 -2.462402 -3.297378 5.003747
## X202 2.961141 -2.411508 -3.661653 4.581390
## X203 3.283539 -2.170680 -2.971820 5.042143
## X204 3.167583 -2.022683 -3.471191 5.551376
## X205 2.923162 -2.306091 -3.957544 4.490698
## X206 2.814210 -2.421819 -3.953366 4.124564
## X207 2.848971 -2.217325 -4.382827 4.378696
## X208 3.008648 -2.433605 -4.197707 4.522074
## X210 2.558002 -2.503234 -4.393290 3.696783
## X212 2.941276 -2.422383 -4.058784 4.517508
## X214 3.241029 -2.296603 -2.458654 4.741335
## X215 3.170106 -2.357781 -3.294138 5.172099
## X216 2.829087 -2.276917 -3.370280 4.660893
## X218 2.861057 -2.519001 -3.467337 4.477566
## X219 3.070840 -2.366271 -3.451754 4.780580
## X220 3.480317 -2.474560 -3.632877 5.725074
## X224 3.008155 -2.277892 -3.740594 4.890764
## X225 2.834389 -2.471596 -4.506230 4.409194
## X226 2.600465 -2.312030 -4.248895 3.801311
## X227 2.738256 -2.250942 -4.820718 4.084542
## X229 3.176803 -2.537928 -3.774873 4.956498
## X230 3.105931 -2.218244 -3.255021 4.881604
## X231 2.948641 -2.170680 -3.908031 4.509879
## X232 3.298795 -2.676116 -4.106215 5.107058
## X233 3.520757 -2.553614 -4.988923 5.547844
## X234 3.325396 -2.390433 -3.881494 5.315680
## X235 2.766948 -2.469348 -4.565949 4.025039
## X236 3.056357 -2.400198 -4.280915 4.890111
## X238 3.066191 -2.482310 -3.463179 4.605738
## X240 3.670715 -2.321564 -3.580922 5.699444
## X241 2.747271 -2.362017 -4.383628 4.014653
## X243 2.900872 -2.344241 -2.827848 4.733688
## X244 3.168424 -2.520368 -3.624216 4.618186
## X245 3.157000 -2.275943 -3.425900 4.906389
## X247 2.858193 -2.629008 -4.154732 4.723921
## X248 2.646884 -2.434974 -2.883833 3.883102
## X249 3.227637 -2.337487 -4.570769 5.191870
## X250 2.703373 -2.289669 -4.399783 4.208655
## X251 3.159550 -2.295609 -3.242144 4.665910
## X252 2.915064 -2.370329 -4.542195 4.316482
## X253 2.986692 -2.242431 -2.984397 4.562778
## X254 2.837908 -2.294617 -4.197707 4.525113
## X255 2.961658 -2.268184 -4.017384 4.485299
## X257 3.359333 -2.379466 -3.043873 5.253674
## X258 2.848971 -2.013654 -3.081726 4.333015
## X260 3.513335 -2.241490 -3.666727 5.913428
## X261 3.298057 -2.302585 -4.149012 5.412105
## X264 2.964242 -2.545931 -4.698932 4.979289
## X265 3.094219 -2.330367 -4.417861 4.827259
## X267 2.941804 -2.334282 -3.329528 4.355967
## X268 3.083743 -2.531244 -3.727205 4.874383
## X269 2.785628 -2.361804 -3.826763 4.412381
## X270 3.015045 -2.223774 -3.039684 4.534207
## X271 2.822569 -2.744351 -5.596723 4.161235
## X273 3.044046 -2.364354 -3.003764 4.748958
## X276 2.854169 -2.099644 -4.207065 4.008967
## X277 2.650421 -2.366697 -4.710753 4.008967
## X278 2.994732 -2.416538 -4.497213 4.464360
## X279 2.881443 -2.532250 -5.244966 4.600594
## X280 2.719979 -2.352196 -4.172739 4.255969
## X281 3.280911 -2.282782 -3.709490 5.232668
## X282 2.640485 -2.549381 -4.239139 3.938613
## X283 2.900322 -2.266253 -3.817167 4.781263
## X284 2.932260 -2.238672 -3.357851 4.525113
## X286 2.912351 -2.477772 -4.827314 4.367359
## X287 3.033028 -2.452827 -2.965009 4.686585
## X288 2.574138 -2.665709 -4.308776 3.654863
## X289 2.993730 -2.523232 -2.493503 4.305672
## X290 2.938633 -2.440354 -4.272276 4.603535
## X291 2.982140 -2.435317 -2.240550 4.288943
## X292 2.949688 -2.408835 -3.856115 4.607206
## X293 2.773838 -2.297598 -3.910524 4.096440
## X294 2.859913 -2.480277 -4.199705 4.574706
## X295 2.623218 -2.336452 -4.439656 3.860909
## X296 2.585506 -2.386184 -4.809369 3.804433
## X297 2.513656 -2.462989 -4.405500 3.573135
## X298 2.898119 -2.305790 -4.600183 4.392389
## X299 2.899772 -2.721744 -4.285263 4.537986
## X301 2.939162 -2.162823 -3.441082 4.610871
## X302 2.990217 -2.470885 -3.390554 4.366547
## X303 3.172203 -2.225624 -3.050822 4.833951
## X304 2.923699 -2.236797 -4.671096 4.482982
## X305 2.899221 -2.424414 -3.629856 4.244873
## X306 3.198265 -2.593740 -3.756302 4.976136
## X307 2.761275 -2.463811 -4.845841 4.143420
## X309 2.542389 -2.606939 -5.587067 3.805473
## X310 2.627563 -2.482669 -5.357855 3.852784
## X311 2.950212 -2.428829 -4.698383 4.633474
## X312 2.753024 -2.574656 -5.112502 4.256821
## X313 2.593013 -2.431101 -3.587045 3.748604
## X314 2.372111 -2.453757 -4.312501 3.334618
## X315 2.923162 -2.231195 -4.266557 4.314822
## X316 2.824351 -2.463811 -5.805151 4.076268
## X317 2.644755 -2.559544 -5.155603 3.756060
## X318 2.937573 -2.328313 -4.098955 4.518270
## X319 2.939162 -2.305790 -2.719617 4.393191
## X320 2.833213 -2.582696 -4.529135 4.121857
## X321 2.783776 -2.243373 -3.154728 4.157683
## X322 2.978586 -2.523232 -4.285989 4.363297
## X323 2.589267 -2.176834 -3.904055 4.199074
## X324 3.068518 -2.145581 -3.716867 4.991235
## X325 2.721953 -2.444955 -4.255923 4.225110
## X326 2.850707 -2.274970 -4.654991 4.200819
## X327 2.555676 -2.374189 -4.481184 3.913012
## X329 3.030617 -2.146436 -3.871361 4.896635
## X330 3.085573 -2.149864 -3.281816 4.534207
## X331 2.741485 -2.354826 -3.428055 4.276316
## X332 2.962692 -2.345597 -3.424978 4.273783
## X333 2.988708 -2.249993 -4.506230 4.576936
## X334 2.693275 -2.488192 -4.944286 4.283059
## X335 2.945491 -2.487350 -4.775721 4.768255
## X336 3.044522 -2.190150 -4.019052 5.070864
## X337 2.655352 -2.357886 -3.770090 3.802352
## X338 3.064792 -2.395139 -3.244963 5.143922
## X339 2.863914 -2.295609 -4.234297 4.654427
## X340 3.189241 -2.235861 -3.926629 4.919334
## X341 2.805782 -2.327800 -3.489045 4.236301
## X342 2.823757 -2.467342 -3.360727 4.366547
## X343 2.705380 -2.270118 -3.975495 4.093700
## X344 3.076390 -2.323094 -3.390851 5.160983
## X346 2.688528 -2.314455 -3.063797 4.054986
## X347 2.939162 -2.478607 -4.513503 4.670202
## X348 2.690565 -2.421932 -4.195713 3.906069
## X350 2.705380 -2.155891 -3.722643 3.885109
## X351 2.837323 -2.582167 -4.963132 4.079030
## X352 2.955951 -2.085057 -2.724332 4.454212
## X353 2.859913 -2.163693 -3.159900 4.407598
## X354 3.248046 -2.278869 -3.716867 5.075113
## X355 2.644045 -2.620864 -3.385226 3.685830
## X357 2.922624 -2.223774 -3.236022 4.506820
## X358 2.785628 -2.436917 -4.835968 4.562030
## X359 2.740195 -2.489758 -3.540804 3.883102
## X360 2.907993 -2.293625 -4.712533 4.519792
## X361 2.894253 -2.598837 -5.361683 4.190330
## X362 3.071303 -2.455503 -3.889772 4.818532
## X364 2.906354 -2.334489 -4.014610 4.552291
## X366 3.080992 -2.391416 -3.960163 4.620376
## X367 3.289521 -2.312131 -3.063155 5.110649
## X369 2.847812 -2.366164 -4.499010 4.625478
## X370 3.086487 -2.241490 -3.568079 4.578422
## X371 3.148024 -2.328724 -3.239844 4.938626
## X372 2.580974 -2.530364 -4.209755 3.675924
## X373 2.714695 -2.301586 -3.621221 4.264469
## X374 2.853593 -2.359579 -3.965951 4.374653
## X375 2.776954 -2.488674 -4.143325 4.121857
## X376 2.776954 -2.314658 -4.010739 4.023154
## X377 3.006672 -2.399867 -2.571380 4.346158
## X378 3.339677 -2.588003 -4.420352 5.217230
## X379 2.718001 -2.492778 -3.511906 4.069812
## X380 2.935451 -2.107018 -3.090263 5.050733
## X381 2.561868 -2.089896 -4.030244 4.150563
## X384 2.861057 -2.261443 -3.295487 4.371414
## X385 2.618855 -2.481353 -3.876173 3.849729
## X386 3.148024 -2.443918 -4.050136 4.981809
## X389 2.740840 -2.481114 -2.707700 4.003267
## X390 3.144583 -2.292635 -3.194915 4.900541
## X391 2.503074 -2.302985 -4.294016 3.667081
## X392 2.823757 -2.264326 -3.929169 4.344519
## X393 2.994231 -2.154165 -3.530851 4.832614
## X394 3.103689 -2.148149 -3.289835 4.787400
## X395 2.874694 -2.273998 -4.115977 4.578422
## X396 2.843746 -2.520119 -4.363794 4.544020
## X397 2.938633 -2.245260 -3.933757 4.680188
## X398 2.859913 -2.520244 -3.359000 4.197328
## X399 2.696652 -2.558639 -4.220588 4.134460
## X400 2.848392 -2.398325 -3.930187 4.479114
## X402 2.389680 -2.422270 -4.419521 4.115529
## X404 2.783158 -2.314759 -4.354411 4.362484
## X405 2.704711 -2.443918 -4.768748 3.796097
## X406 2.922624 -2.298593 -3.847172 4.562030
## X407 2.698673 -2.354405 -4.385232 4.064264
## X408 3.061988 -2.583490 -3.105547 4.666626
## X409 3.028199 -2.267218 -3.585601 4.549287
## X410 2.885917 -2.443573 -3.767923 4.796917
## X411 2.866193 -2.423849 -4.610484 5.256500
## X412 2.823163 -2.228406 -4.671844 4.625478
## X413 3.076390 -2.529611 -3.621595 4.735776
## X414 3.096030 -2.463341 -3.572698 4.971715
## X415 3.394844 -2.486508 -4.249596 5.289608
## X416 3.052585 -2.325547 -3.489045 4.680900
## X418 3.048325 -2.189256 -3.247018 4.712008
## X419 2.498974 -2.432124 -4.371680 3.803393
## X421 2.946542 -2.459707 -3.888795 4.664478
## X422 2.637628 -2.272056 -2.948086 3.946432
## X423 2.773838 -2.218244 -3.909526 4.072581
## X424 2.951258 -2.401632 -3.572342 4.556042
## X425 2.950735 -2.230264 -3.971242 4.374653
## X426 3.057768 -2.511210 -5.167816 4.800985
## X427 2.706716 -2.321156 -3.252691 4.241448
## X429 2.810607 -2.507030 -5.073096 4.129067
## X430 2.871868 -2.538814 -4.444753 4.188577
## X431 3.114848 -2.307899 -2.778526 4.706382
## X432 2.872434 -2.249993 -3.412764 4.353519
## X433 2.972464 -2.177716 -3.606378 4.523594
## X435 2.829678 -2.416426 -4.095345 4.151454
## X436 2.976549 -2.244316 -4.022396 4.923849
## X437 2.972464 -2.392948 -4.296216 4.470584
## X438 2.771338 -2.470057 -4.346659 4.242305
## X439 2.975530 -2.443688 -4.514416 4.737167
## X440 2.751110 -2.529988 -4.684430 4.039126
## X441 2.844909 -2.417435 -3.070887 4.656584
## X443 2.759377 -2.428489 -4.460204 3.862936
## X446 3.214466 -2.273026 -3.899600 4.895332
## X447 3.333275 -2.302885 -3.904551 5.378924
## X448 2.871302 -2.388252 -4.447312 4.339596
## X449 2.962175 -2.478368 -3.888306 4.763445
## X450 3.021400 -2.334695 -3.875209 5.004371
## X451 3.069912 -2.716133 -2.802965 4.748958
## X452 3.218876 -2.271086 -3.948168 4.934138
## X453 3.340385 -2.472543 -3.653898 5.343130
## X456 3.424914 -2.381087 -4.385232 5.539246
## X457 3.377246 -2.369045 -3.722229 5.393426
## X458 3.228826 -2.431442 -4.818116 5.135645
## X459 3.224062 -2.480636 -4.713424 4.992489
## X461 3.301377 -2.312837 -3.808114 5.150996
## X464 2.910174 -2.464163 -4.073954 4.442448
## X465 2.902520 -2.594811 -4.204383 4.432205
## X466 3.002211 -2.490844 -2.709501 4.556042
## X467 3.032064 -2.444725 -3.448604 4.553042
## X468 2.895912 -2.487590 -4.376442 4.489157
## X469 3.149740 -2.376339 -2.655695 4.796239
## X470 2.900322 -2.141317 -3.440146 4.548535
## X471 2.917230 -2.413964 -3.917538 4.565019
## X473 2.703373 -2.513553 -4.034191 3.934694
## X474 3.400197 -2.564080 -4.798391 5.352397
## X475 2.748552 -2.295609 -3.476029 4.042868
## X476 2.755570 -2.403511 -4.019608 4.042868
## X477 3.021887 -2.415642 -3.414891 4.684455
## X478 2.810607 -2.684138 -4.331334 4.261073
## X479 2.680336 -2.257612 -3.856588 4.269554
## X480 2.970927 -2.276917 -2.905892 4.364923
## X481 2.892037 -2.398325 -4.094745 4.727414
## X482 2.956991 -2.526854 -4.526359 4.624021
## X483 2.643334 -2.233992 -4.232228 3.944480
## X485 2.423031 -2.260484 -4.006334 3.500171
## X486 2.797891 -2.352406 -2.594141 4.194706
## X487 2.824351 -2.448652 -4.273710 4.551541
## X488 2.934920 -2.217325 -3.563834 4.897287
## X490 3.005683 -2.590667 -4.236369 4.634199
## X491 3.110845 -2.502012 -4.418691 4.999375
## X492 2.582487 -2.546186 -4.794637 3.954223
## X495 3.022374 -2.612513 -3.886355 4.768255
## X496 3.006178 -2.344762 -4.289630 4.769627
## X499 2.863343 -2.290657 -3.437654 4.351068
## X500 3.055886 -2.221005 -3.578770 4.921270
## X503 2.792391 -2.155891 -4.156007 4.226835
## X504 2.987196 -2.370650 -3.394420 4.430625
## X505 2.554899 -1.811554 -3.091803 3.746469
## X506 2.575661 -2.075450 -3.018387 3.916970
## X508 2.840247 -2.125276 -3.751606 4.169207
## X510 3.175968 -2.134532 -3.007805 5.257064
## X511 2.687167 -2.513430 -3.240099 3.873042
## X512 2.687847 -2.468404 -4.425352 3.871024
## X513 3.021400 -2.201835 -3.787595 4.849274
## X514 2.614472 -2.319528 -4.010739 3.836443
## X515 2.948116 -2.384338 -4.116590 4.740641
## X516 2.923699 -2.254748 -4.109864 4.363297
## X517 3.024320 -2.236797 -3.975495 4.607940
## X518 3.008648 -2.266253 -3.770958 4.535719
## X519 2.902520 -2.105375 -3.759731 4.469807
## X520 2.815409 -2.184802 -4.159844 4.255969
## X521 2.631889 -1.987045 -3.673006 3.897110
## X522 3.072693 -2.273026 -3.438276 4.660893
## X523 2.987196 -2.463811 -5.132803 4.624750
## X524 2.927453 -2.311021 -3.793796 4.565765
## X528 2.507157 -2.407612 -4.691927 4.035378
## X529 2.577942 -2.081043 -3.427439 3.636967
## X530 2.598235 -2.207275 -4.462803 3.680332
## X533 2.793004 -2.377632 -4.929793 4.120954
## X535 2.869035 -2.334385 -3.387886 4.630569
## X536 3.037833 -2.257612 -3.643524 4.554542
## X538 3.196221 -2.090705 -3.353837 5.011847
## X539 3.238286 -2.513553 -4.636454 4.931570
## X540 3.236323 -2.445532 -2.740005 4.993116
## X541 2.670002 -2.304186 -3.191261 4.073504
## X542 3.218476 -2.426223 -3.067658 4.983068
## X543 3.235536 -2.491931 -4.446458 5.018060
## X545 3.030134 -2.345701 -3.829522 4.499157
## X547 2.794228 -2.360850 -4.927168 4.258523
## X549 2.962175 -2.466163 -4.489167 4.562778
## X552 3.110845 -2.346955 -3.489701 4.754487
## X553 3.382015 -2.491810 -4.395720 5.238363
## X554 3.088311 -2.381628 -3.998671 4.522074
## X555 3.364533 -2.510471 -3.838308 5.223531
## X558 3.327910 -2.510471 -4.488276 5.136237
## X559 3.121483 -2.468286 -3.070671 4.685165
## X560 3.175133 -2.379358 -3.512576 5.303509
## X561 3.301377 -2.309710 -3.620100 5.072078
## X562 3.379974 -2.597090 -4.724179 5.365966
## X563 3.421653 -2.255702 -3.027429 5.598355
## X564 3.222469 -2.208184 -3.144232 4.832614
## X565 3.108614 -2.198225 -3.543568 4.622564
## X566 3.341093 -2.324831 -3.720164 5.363258
## X567 3.335058 -2.470412 -3.288494 5.129122
## X568 3.378611 -2.138767 -2.787418 5.425895
## X569 3.200304 -2.944469 -5.368740 4.895984
## X570 2.339881 -2.133687 -3.015119 3.845649
## X571 2.877512 -2.468168 -4.336671 4.393994
## X572 3.056357 -2.210918 -3.217377 4.558289
## X573 3.014554 -1.948413 -2.595883 4.629842
## X574 2.663053 -2.299590 -3.704602 3.777223
## X576 2.994732 -2.357781 -4.281638 4.712710
## X577 3.036394 -2.129472 -3.496938 4.746189
## X579 3.179719 -2.131999 -2.628731 5.491708
## X580 3.145875 -2.500305 -4.681080 5.114832
## X582 3.210844 -2.328929 -2.489276 4.867801
## X584 3.118392 -2.179483 -2.824135 5.000625
## X585 3.315639 -2.172434 -3.160607 5.301845
## X586 3.002211 -2.315974 -4.455028 4.928999
## X587 3.029167 -2.145581 -3.688480 4.967287
## X588 3.097837 -2.319630 -3.967007 4.928999
## X591 2.520917 -2.278869 -4.246098 3.668189
## X592 2.657458 -2.232127 -2.932194 4.017490
## X593 3.137232 -2.361486 -4.374852 5.214935
## X594 3.062456 -2.188364 -3.972835 4.972347
## X595 2.797281 -2.131999 -3.270432 4.226835
## X596 3.069447 -2.249993 -3.488391 5.074506
## X597 3.008155 -2.360214 -3.603803 4.684455
## X598 3.229618 -2.223774 -3.487736 5.278432
## X599 2.711378 -2.318003 -3.495618 4.058702
## X600 3.223266 -2.240550 -3.389071 5.122583
## X602 3.177220 -2.122767 -3.479591 5.005619
## X604 2.883683 -2.263364 -3.551555 4.684455
## X605 3.072230 -2.342366 -3.689280 4.806397
## X606 3.078233 -2.320444 -3.508226 4.895332
## X607 2.913437 -2.409836 -5.318724 4.345339
## X608 3.226844 -2.365844 -4.515329 4.533450
## X609 3.035914 -2.286712 -3.799141 4.594701
## X610 3.071767 -2.505681 -4.508043 4.888151
## X612 3.211247 -2.398986 -2.296603 5.072078
## X613 3.009635 -2.262403 -3.841099 4.736472
## X614 3.082369 -2.331602 -4.280192 4.864503
## X615 2.867899 -2.208184 -3.220377 4.219926
## X616 2.823757 -2.453408 -4.106822 4.274627
## X618 2.683074 -2.272056 -4.249596 4.165667
## X620 3.072693 -2.449115 -4.629668 4.572474
## X621 2.793616 -2.565900 -4.442201 4.376271
## X622 2.903617 -2.493625 -4.781907 4.220791
## X623 2.928524 -2.164564 -3.519643 4.451081
## X624 3.091951 -2.401743 -4.575611 4.980549
## X626 2.921547 -2.250942 -3.769656 4.746189
## X627 3.072230 -2.174192 -3.556098 4.917397
## X629 2.467252 -2.327698 -4.551629 3.639212
## X630 2.700018 -2.176834 -4.510770 3.857866
## X631 3.043570 -2.085057 -3.453965 4.668773
## X633 2.629007 -2.561226 -3.234497 4.031624
## X634 3.171365 -2.187472 -3.631366 5.090232
## X635 3.175551 -2.143873 -3.767923 5.085404
## X636 3.044999 -2.259526 -4.042701 4.972347
## X637 2.946542 -2.508503 -4.686814 4.428254
## X638 2.852439 -2.238672 -2.452711 4.332192
## X639 2.802754 -2.319630 -5.182848 4.080869
## X640 3.059176 -2.406946 -4.103184 4.635650
## X641 2.683758 -2.324524 -2.367871 3.669295
## X642 3.199489 -2.233992 -2.879551 5.111247
## X643 2.759377 -2.295609 -3.880040 4.179793
## X644 2.804572 -2.389015 -4.006883 4.377888
## X645 2.978077 -2.389451 -3.815350 4.484527
## X647 2.781920 -2.239610 -2.840611 4.001364
## X648 3.176803 -2.051048 -2.683114 4.982438
## X650 3.043093 -2.205458 -4.071019 5.009980
## X651 2.763800 -2.227478 -3.331205 4.376271
## X652 3.215269 -2.241490 -2.865933 5.099260
## X653 3.269189 -2.107841 -2.805112 5.044600
## X654 2.750471 -2.330676 -4.010739 4.510643
## X655 2.918851 -2.315265 -4.105001 4.714115
## X656 3.066191 -2.359791 -3.512241 4.821893
## X658 3.081910 -2.433605 -3.691683 4.904441
## X659 2.723924 -2.178599 -3.120842 3.936655
## X660 3.178887 -2.410839 -4.007433 4.812472
## X661 3.125005 -2.385967 -3.711534 4.581390
## X663 2.906901 -2.280824 -4.205723 4.588794
## X664 2.987196 -2.264326 -3.292792 4.458901
## X666 2.881443 -2.258568 -4.440504 4.185067
## X667 2.992728 -2.278869 -4.224681 4.614531
## X668 2.552565 -2.409836 -4.319991 3.828226
## X669 2.984166 -2.327698 -3.542185 4.927712
## X670 3.218076 -2.355142 -4.207737 5.196499
## X671 2.597491 -2.145581 -4.524512 4.060557
## X672 3.021400 -2.524105 -5.099794 5.051957
## X673 2.965273 -2.297598 -3.818533 4.653708
## X674 2.959587 -2.303686 -3.808114 4.385955
## X675 2.744704 -1.967542 -3.536330 4.311499
## X676 2.908539 -2.169804 -3.767923 4.822564
## X678 2.979095 -2.020418 -2.445532 4.737167
## X680 2.832625 -2.266253 -3.529485 4.232863
## X681 3.033028 -2.309308 -3.208431 4.553792
## X682 2.978077 -2.546314 -2.597493 4.419537
## X683 3.005187 -2.187472 -3.284215 4.340417
## X684 2.761907 -2.162823 -3.810821 4.067964
## X686 2.757475 -2.357886 -2.694147 3.818947
## X688 3.131573 -2.158485 -3.224894 4.904441
## X690 2.381396 -2.367337 -4.180556 3.702239
## X691 2.840247 -2.249993 -3.862757 4.510643
## X692 3.005683 -1.933093 -2.322176 4.440088
## X693 2.387845 -2.206366 -4.361440 3.703328
## X694 2.796671 -2.642965 -3.420380 4.340417
## X696 3.206398 -2.379682 -4.444753 5.217803
## X697 2.939691 -2.498965 -3.600502 4.573218
## X698 2.796671 -2.162823 -3.173663 3.945456
## X699 3.223664 -2.287696 -3.448604 5.096856
## X700 2.587012 -2.238672 -3.706636 3.894116
## X702 3.069912 -2.294617 -3.936316 4.979920
## X703 2.634045 -2.357886 -4.189755 4.033502
## X705 3.112181 -2.401853 -4.421183 5.084195
## X707 2.733718 -2.339353 -4.185802 4.279690
## X708 2.866193 -2.148149 -3.358138 4.229421
## X710 2.482404 -2.380547 -5.175038 3.488165
## X711 2.893146 -2.330882 -3.943514 4.538741
## X712 2.851284 -2.214574 -4.054163 4.648665
## X713 2.767576 -2.444494 -4.158563 4.262772
## X714 2.706048 -2.551944 -4.027995 4.167437
## X716 2.808197 -2.215490 -3.315111 4.621105
## X717 2.932260 -2.508626 -3.020640 4.553792
## X718 2.719979 -2.305590 -3.773566 4.089126
## X719 2.885359 -2.532753 -4.140179 4.316482
## X720 3.033991 -2.175952 -4.472389 4.449513
## X721 3.030134 -2.363929 -2.915813 4.853256
## X722 2.730464 -2.233059 -2.344866 4.055916
## X723 2.571084 -2.327493 -4.712199 3.737909
## X724 2.730464 -2.366164 -3.903559 4.147887
## X725 2.887033 -2.447149 -4.159203 4.534963
## X727 2.968361 -2.597628 -3.626468 4.741335
## X728 2.544747 -2.373974 -4.626496 3.953251
## X729 2.561868 -2.588269 -5.093908 3.932732
## X730 3.004692 -2.217325 -3.727620 4.608673
## X731 2.768832 -2.442537 -3.488391 3.894116
## X732 2.898671 -2.189256 -3.782311 4.621834
## X733 3.100993 -2.290657 -3.449863 4.783310
## X735 2.983660 -2.474442 -4.830441 4.579906
## X736 2.273156 -2.344032 -4.623742 3.221497
## X737 2.933857 -2.423059 -3.700952 4.615263
## X738 3.205993 -2.254748 -3.314836 5.020540
## X739 2.830268 -2.317191 -4.290359 4.360856
## X740 2.516890 -2.274970 -4.439656 3.665973
## X741 2.977059 -2.402626 -4.544075 4.863182
## X742 2.475698 -2.073857 -3.763172 3.815845
## X743 2.688528 -2.296603 -3.853283 3.792962
## X744 2.718001 -2.431328 -4.588313 4.028805
## X745 2.670694 -2.392729 -4.827439 3.815845
## X746 2.893700 -2.333147 -2.429510 4.471360
## X748 3.100993 -2.772429 -6.095937 4.806397
## X749 2.569554 -2.437374 -5.057098 3.721768
## X750 3.085116 -2.212744 -3.674188 5.052569
## X751 3.279783 -2.170680 -3.045133 5.090835
## X753 2.702703 -2.401411 -3.289298 3.883102
## X754 3.109507 -2.401632 -4.272276 4.738557
## X755 2.715357 -2.378710 -4.422849 4.207786
## X756 2.922086 -2.454804 -4.690619 4.619646
## X757 2.844328 -2.325444 -4.743973 4.225973
## X758 2.855895 -2.295609 -4.735735 4.628388
## X759 2.766319 -2.515778 -3.811273 4.065190
## X760 3.140698 -2.230264 -1.999522 5.304618
## X761 3.063858 -2.436231 -4.022955 4.401206
## X762 2.902520 -2.666429 -5.000289 4.177151
## X764 3.144583 -2.259526 -2.971625 4.721123
## X766 3.104138 -2.120264 -3.396807 5.122583
## X767 3.083743 -2.607617 -2.938218 4.495315
## X768 3.113071 -2.462402 -3.297378 5.003747
## X769 3.006672 -2.315468 -4.137043 4.879637
## X770 2.973487 -2.344866 -4.029119 4.761381
## X771 2.961141 -2.411508 -3.661653 4.581390
## X772 3.283539 -2.170680 -2.971820 5.042143
## X773 3.167583 -2.022683 -3.471191 5.551376
## X774 2.923162 -2.306091 -3.957544 4.490698
## X776 2.848971 -2.217325 -4.382827 4.378696
## X777 3.008648 -2.433605 -4.197707 4.522074
## X778 3.115292 -2.300587 -3.492984 4.815841
## X779 2.558002 -2.503234 -4.393290 3.696783
## X781 2.941276 -2.422383 -4.058784 4.517508
## X782 2.916148 -2.169804 -3.585601 3.959079
## X783 3.241029 -2.296603 -2.458654 4.741335
## X784 3.170106 -2.357781 -3.294138 5.172099
## X785 2.829087 -2.276917 -3.370280 4.660893
## X787 2.861057 -2.519001 -3.467337 4.477566
## X788 3.070840 -2.366271 -3.451754 4.780580
## X789 3.480317 -2.474560 -3.632877 5.725074
## X790 2.577182 -2.338627 -4.077487 3.743263
## X791 2.631889 -2.252843 -3.766193 3.825137
## X793 3.008155 -2.277892 -3.740594 4.890764
## X794 2.834389 -2.471596 -4.506230 4.409194
## X795 2.600465 -2.312030 -4.248895 3.801311
## X797 2.741485 -2.480397 -3.439834 4.039126
## X798 3.176803 -2.537928 -3.774873 4.956498
## X799 3.105931 -2.218244 -3.255021 4.881604
## X800 2.948641 -2.170680 -3.908031 4.509879
## X801 3.298795 -2.676116 -4.106215 5.107058
## X802 3.520757 -2.553614 -4.988923 5.547844
## X803 3.325396 -2.390433 -3.881494 5.315680
## X804 2.766948 -2.469348 -4.565949 4.025039
## X805 3.056357 -2.400198 -4.280915 4.890111
## X806 3.294725 -2.352931 -3.553300 5.152173
## X807 3.066191 -2.482310 -3.463179 4.605738
## X808 3.326833 -2.498235 -3.559607 5.484477
## X810 2.747271 -2.362017 -4.383628 4.014653
## X811 2.710713 -2.535022 -5.312416 4.136255
## X812 2.900872 -2.344241 -2.827848 4.733688
## X814 3.157000 -2.275943 -3.425900 4.906389
## X815 2.988708 -2.234926 -4.278748 4.835955
## X817 2.646884 -2.434974 -2.883833 3.883102
## X819 2.703373 -2.289669 -4.399783 4.208655
## X821 2.915064 -2.370329 -4.542195 4.316482
## X822 2.986692 -2.242431 -2.984397 4.562778
## X824 2.961658 -2.268184 -4.017384 4.485299
## X825 2.836150 -2.210918 -3.619727 4.283900
## X826 3.359333 -2.379466 -3.043873 5.253674
## X827 2.848971 -2.013654 -3.081726 4.333015
## X828 3.144152 -2.199126 -2.814244 4.977398
## X829 3.513335 -2.241490 -3.666727 5.913428
## X830 3.298057 -2.302585 -4.149012 5.412105
## X831 3.138100 -2.446225 -4.504420 4.966653
## X832 3.096934 -2.408057 -2.816582 4.683033
## X833 2.964242 -2.545931 -4.698932 4.979289
## X834 3.094219 -2.330367 -4.417861 4.827259
## X835 3.437851 -2.357147 -4.214480 5.806493
## X836 2.941804 -2.334282 -3.329528 4.355967
## X837 3.083743 -2.531244 -3.727205 4.874383
## X838 2.785628 -2.361804 -3.826763 4.412381
## X839 3.015045 -2.223774 -3.039684 4.534207
## X840 2.822569 -2.744351 -5.596723 4.161235
## X841 2.568022 -2.319324 -4.490057 3.725005
## X842 3.044046 -2.364354 -3.003764 4.748958
## X843 2.751748 -2.403843 -4.540319 4.181552
## X844 3.197856 -2.424188 -4.449022 5.162741
## X845 2.854169 -2.099644 -4.207065 4.008967
## X846 2.650421 -2.366697 -4.710753 4.008967
## X847 2.994732 -2.416538 -4.497213 4.464360
## X848 2.881443 -2.532250 -5.244966 4.600594
## X849 2.719979 -2.352196 -4.172739 4.255969
## X851 2.640485 -2.549381 -4.239139 3.938613
## X852 2.900322 -2.266253 -3.817167 4.781263
## X853 2.932260 -2.238672 -3.357851 4.525113
## X855 2.912351 -2.477772 -4.827314 4.367359
## X856 3.033028 -2.452827 -2.965009 4.686585
## X857 2.574138 -2.665709 -4.308776 3.654863
## X858 2.993730 -2.523232 -2.493503 4.305672
## X859 2.938633 -2.440354 -4.272276 4.603535
## X860 2.982140 -2.435317 -2.240550 4.288943
## X861 2.949688 -2.408835 -3.856115 4.607206
## X862 2.773838 -2.297598 -3.910524 4.096440
## X863 2.859913 -2.480277 -4.199705 4.574706
## X864 2.623218 -2.336452 -4.439656 3.860909
## X866 2.513656 -2.462989 -4.405500 3.573135
## X868 2.899772 -2.721744 -4.285263 4.537986
## X869 3.139400 -2.287696 -4.238446 4.458120
## X871 2.990217 -2.470885 -3.390554 4.366547
## X872 3.172203 -2.225624 -3.050822 4.833951
## X873 2.923699 -2.236797 -4.671096 4.482982
## X874 2.899221 -2.424414 -3.629856 4.244873
## X875 3.198265 -2.593740 -3.756302 4.976136
## X877 2.667228 -2.658546 -5.321995 4.109184
## X878 2.542389 -2.606939 -5.587067 3.805473
## X879 2.627563 -2.482669 -5.357855 3.852784
## X880 2.950212 -2.428829 -4.698383 4.633474
## X881 2.753024 -2.574656 -5.112502 4.256821
## X883 2.372111 -2.453757 -4.312501 3.334618
## X884 2.923162 -2.231195 -4.266557 4.314822
## X885 2.824351 -2.463811 -5.805151 4.076268
## X887 2.937573 -2.328313 -4.098955 4.518270
## X888 2.939162 -2.305790 -2.719617 4.393191
## X889 2.833213 -2.582696 -4.529135 4.121857
## X890 2.783776 -2.243373 -3.154728 4.157683
## X891 2.978586 -2.523232 -4.285989 4.363297
## X892 2.589267 -2.176834 -3.904055 4.199074
## X893 3.068518 -2.145581 -3.716867 4.991235
## X894 2.721953 -2.444955 -4.255923 4.225110
## X897 2.886475 -2.566160 -4.978120 4.298995
## X898 3.030617 -2.146436 -3.871361 4.896635
## X899 3.085573 -2.149864 -3.281816 4.534207
## X900 2.741485 -2.354826 -3.428055 4.276316
## X901 2.962692 -2.345597 -3.424978 4.273783
## X902 2.988708 -2.249993 -4.506230 4.576936
## X903 2.693275 -2.488192 -4.944286 4.283059
## X904 2.945491 -2.487350 -4.775721 4.768255
## X907 3.064792 -2.395139 -3.244963 5.143922
## X908 2.863914 -2.295609 -4.234297 4.654427
## X909 3.189241 -2.235861 -3.926629 4.919334
## X910 2.805782 -2.327800 -3.489045 4.236301
## X911 2.823757 -2.467342 -3.360727 4.366547
## X912 2.705380 -2.270118 -3.975495 4.093700
## X914 2.737609 -2.162823 -4.512591 3.928802
## X915 2.688528 -2.314455 -3.063797 4.054986
## X916 2.939162 -2.478607 -4.513503 4.670202
## X918 2.774462 -2.399537 -4.762058 4.173623
## X920 2.837323 -2.582167 -4.963132 4.079030
## X921 2.955951 -2.085057 -2.724332 4.454212
## X924 2.644045 -2.620864 -3.385226 3.685830
## X926 2.922624 -2.223774 -3.236022 4.506820
## X927 2.785628 -2.436917 -4.835968 4.562030
## X929 2.907993 -2.293625 -4.712533 4.519792
## X930 2.894253 -2.598837 -5.361683 4.190330
## X931 3.071303 -2.455503 -3.889772 4.818532
## X933 2.906354 -2.334489 -4.014610 4.552291
## X934 2.830268 -2.533635 -4.382027 4.252561
## X935 3.080992 -2.391416 -3.960163 4.620376
## X937 2.891482 -2.382603 -4.277306 4.444020
## X938 2.847812 -2.366164 -4.499010 4.625478
## X939 3.086487 -2.241490 -3.568079 4.578422
## X940 3.148024 -2.328724 -3.239844 4.938626
## X942 2.714695 -2.301586 -3.621221 4.264469
## X943 2.853593 -2.359579 -3.965951 4.374653
## X944 2.776954 -2.488674 -4.143325 4.121857
## X945 2.776954 -2.314658 -4.010739 4.023154
## X946 3.006672 -2.399867 -2.571380 4.346158
## X947 3.339677 -2.588003 -4.420352 5.217230
## X948 2.718001 -2.492778 -3.511906 4.069812
## X949 2.935451 -2.107018 -3.090263 5.050733
## X951 2.703373 -2.527355 -3.961739 4.177151
## X952 3.123246 -2.668589 -3.087848 4.785356
## X954 2.618855 -2.481353 -3.876173 3.849729
## X955 3.148024 -2.443918 -4.050136 4.981809
## X957 2.782539 -2.655553 -4.258041 4.100089
## X958 2.740840 -2.481114 -2.707700 4.003267
## X959 3.144583 -2.292635 -3.194915 4.900541
## X960 2.503074 -2.302985 -4.294016 3.667081
## X961 2.823757 -2.264326 -3.929169 4.344519
## X962 2.994231 -2.154165 -3.530851 4.832614
## X963 3.103689 -2.148149 -3.289835 4.787400
## X964 2.874694 -2.273998 -4.115977 4.578422
## X965 2.843746 -2.520119 -4.363794 4.544020
## X966 2.938633 -2.245260 -3.933757 4.680188
## X968 2.696652 -2.558639 -4.220588 4.134460
## X969 2.848392 -2.398325 -3.930187 4.479114
## X970 3.045474 -2.095571 -3.291984 4.721123
## X971 2.389680 -2.422270 -4.419521 4.115529
## X972 2.906354 -2.610334 -3.293330 4.488386
## X975 2.922624 -2.298593 -3.847172 4.562030
## X977 3.061988 -2.583490 -3.105547 4.666626
## X978 3.028199 -2.267218 -3.585601 4.549287
## X979 2.885917 -2.443573 -3.767923 4.796917
## X981 2.823163 -2.228406 -4.671844 4.625478
## X982 3.076390 -2.529611 -3.621595 4.735776
## X983 3.096030 -2.463341 -3.572698 4.971715
## X984 3.394844 -2.486508 -4.249596 5.289608
## X986 3.077312 -2.259526 -4.089954 4.952042
## X988 2.498974 -2.432124 -4.371680 3.803393
## X989 3.063858 -2.284745 -4.662587 4.799630
## X990 2.946542 -2.459707 -3.888795 4.664478
## X991 2.637628 -2.272056 -2.948086 3.946432
## X992 2.773838 -2.218244 -3.909526 4.072581
## X994 2.950735 -2.230264 -3.971242 4.374653
## X995 3.057768 -2.511210 -5.167816 4.800985
## X996 2.706716 -2.321156 -3.252691 4.241448
## X997 3.090133 -2.430305 -3.840633 5.002499
## X998 2.810607 -2.507030 -5.073096 4.129067
## X999 2.871868 -2.538814 -4.444753 4.188577
## X1000 3.114848 -2.307899 -2.778526 4.706382
## X1001 2.872434 -2.249993 -3.412764 4.353519
## X1002 2.972464 -2.177716 -3.606378 4.523594
## X1003 3.089678 -2.284745 -3.197114 4.932212
## X1004 2.829678 -2.416426 -4.095345 4.151454
## X1005 2.976549 -2.244316 -4.022396 4.923849
## X1006 2.972464 -2.392948 -4.296216 4.470584
## X1007 2.771338 -2.470057 -4.346659 4.242305
## X1008 2.975530 -2.443688 -4.514416 4.737167
## X1009 2.751110 -2.529988 -4.684430 4.039126
## X1011 3.235536 -2.485187 -3.144696 5.207462
## X1012 2.759377 -2.428489 -4.460204 3.862936
## X1013 2.907993 -2.508134 -4.319240 4.385955
## X1014 2.824351 -2.413852 -4.160484 4.279690
## X1016 3.333275 -2.302885 -3.904551 5.378924
## X1017 2.871302 -2.388252 -4.447312 4.339596
## X1018 2.962175 -2.478368 -3.888306 4.763445
## X1019 3.021400 -2.334695 -3.875209 5.004371
## X1020 3.069912 -2.716133 -2.802965 4.748958
## X1021 3.218876 -2.271086 -3.948168 4.934138
## X1022 3.340385 -2.472543 -3.653898 5.343130
## X1024 2.841998 -2.455387 -4.763111 4.290621
## X1026 3.377246 -2.369045 -3.722229 5.393426
## X1027 3.228826 -2.431442 -4.818116 5.135645
## X1028 3.224062 -2.480636 -4.713424 4.992489
## X1029 3.339322 -2.527731 -4.366153 5.290165
## X1031 3.268428 -2.221927 -2.923598 4.960311
## X1032 3.295466 -2.659975 -4.086972 4.998750
## X1033 2.910174 -2.464163 -4.073954 4.442448
## X1035 3.002211 -2.490844 -2.709501 4.556042
## X1036 3.032064 -2.444725 -3.448604 4.553042
## X1037 2.895912 -2.487590 -4.376442 4.489157
## X1038 3.149740 -2.376339 -2.655695 4.796239
## X1039 2.900322 -2.141317 -3.440146 4.548535
## X1040 2.917230 -2.413964 -3.917538 4.565019
## X1041 3.337192 -2.435888 -4.163695 5.081777
## X1042 2.703373 -2.513553 -4.034191 3.934694
## X1044 2.748552 -2.295609 -3.476029 4.042868
## X1048 2.680336 -2.257612 -3.856588 4.269554
## X1049 2.970927 -2.276917 -2.905892 4.364923
## X1050 2.892037 -2.398325 -4.094745 4.727414
## X1051 2.956991 -2.526854 -4.526359 4.624021
## X1053 2.870169 -2.307598 -4.805330 4.403605
## X1054 2.423031 -2.260484 -4.006334 3.500171
## X1055 2.797891 -2.352406 -2.594141 4.194706
## X1056 2.824351 -2.448652 -4.273710 4.551541
## X1057 2.934920 -2.217325 -3.563834 4.897287
## X1058 2.783158 -2.182139 -4.348979 4.243161
## X1059 3.005683 -2.590667 -4.236369 4.634199
## X1062 3.023347 -2.301586 -3.611918 4.597650
## X1063 2.551786 -2.607481 -4.602175 3.744333
## X1064 3.022374 -2.612513 -3.886355 4.768255
## X1065 3.006178 -2.344762 -4.289630 4.769627
## X1067 2.851284 -2.415978 -4.289630 4.467474
## X1069 3.055886 -2.221005 -3.578770 4.921270
## X1070 2.817801 -2.314354 -4.006883 4.141631
## X1073 2.987196 -2.370650 -3.394420 4.430625
## X1074 2.554899 -1.811554 -3.091803 3.746469
## X1075 2.575661 -2.075450 -3.018387 3.916970
## X1076 2.997730 -2.210918 -3.817622 4.454212
## X1078 2.753661 -2.361592 -4.420352 3.889116
## X1079 3.175968 -2.134532 -3.007805 5.257064
## X1080 2.687167 -2.513430 -3.240099 3.873042
## X1081 2.687847 -2.468404 -4.425352 3.871024
## X1082 3.021400 -2.201835 -3.787595 4.849274
## X1083 2.614472 -2.319528 -4.010739 3.836443
## X1084 2.948116 -2.384338 -4.116590 4.740641
## X1085 2.923699 -2.254748 -4.109864 4.363297
## X1086 3.024320 -2.236797 -3.975495 4.607940
## X1087 3.008648 -2.266253 -3.770958 4.535719
## X1088 2.902520 -2.105375 -3.759731 4.469807
## X1089 2.815409 -2.184802 -4.159844 4.255969
## X1091 3.072693 -2.273026 -3.438276 4.660893
## X1092 2.987196 -2.463811 -5.132803 4.624750
## X1093 2.927453 -2.311021 -3.793796 4.565765
## X1095 2.572612 -2.267218 -4.038721 3.957138
## X1096 2.931194 -2.230264 -4.238446 4.530422
## X1099 2.598235 -2.207275 -4.462803 3.680332
## X1100 2.865624 -2.232127 -4.009085 4.735080
## X1101 2.996732 -2.286712 -4.058784 4.792163
## X1102 2.793004 -2.377632 -4.929793 4.120954
## X1103 3.028683 -2.390761 -3.477323 4.676626
## X1104 2.869035 -2.334385 -3.387886 4.630569
## X1107 3.196221 -2.090705 -3.353837 5.011847
## X1108 3.238286 -2.513553 -4.636454 4.931570
## X1110 2.670002 -2.304186 -3.191261 4.073504
## X1111 3.218476 -2.426223 -3.067658 4.983068
## X1112 3.235536 -2.491931 -4.446458 5.018060
## X1114 3.030134 -2.345701 -3.829522 4.499157
## X1115 3.145445 -2.380979 -3.863709 4.811124
## X1116 2.794228 -2.360850 -4.927168 4.258523
## X1117 2.808197 -2.421707 -3.478943 4.281375
## X1118 2.962175 -2.466163 -4.489167 4.562778
## X1119 3.186766 -2.502012 -3.979232 4.965386
## X1120 3.067122 -2.599510 -4.506230 4.500691
## X1121 3.110845 -2.346955 -3.489701 4.754487
## X1122 3.382015 -2.491810 -4.395720 5.238363
## X1123 3.088311 -2.381628 -3.998671 4.522074
## X1124 3.364533 -2.510471 -3.838308 5.223531
## X1125 3.318178 -2.404618 -3.598673 5.175599
## X1126 2.975019 -2.299590 -3.806762 4.351068
## X1127 3.327910 -2.510471 -4.488276 5.136237
## X1128 3.121483 -2.468286 -3.070671 4.685165
## X1130 3.301377 -2.309710 -3.620100 5.072078
## X1131 3.379974 -2.597090 -4.724179 5.365966
## X1132 3.421653 -2.255702 -3.027429 5.598355
## X1133 3.222469 -2.208184 -3.144232 4.832614
## X1134 3.108614 -2.198225 -3.543568 4.622564
## X1135 3.341093 -2.324831 -3.720164 5.363258
## X1137 3.378611 -2.138767 -2.787418 5.425895
## smoothness_worst symmetry_worst
## X1 -1.401837 -0.9485186
## X2 -1.552206 -1.8138504
## X3 -1.468032 -1.3273311
## X4 -1.246824 -0.4547732
## X5 -1.495633 -2.1134503
## X6 -1.343543 -1.1682237
## X7 -1.468808 -1.6137366
## X9 -1.373392 -1.0226796
## X10 -1.323124 -1.0268307
## X11 -1.577215 -1.6835473
## X12 -1.486854 -1.2478490
## X14 -1.599859 -1.7735849
## X15 -1.391541 -1.3351867
## X17 -1.460319 -1.6339618
## X18 -1.344209 -1.2853171
## X20 -1.469584 -1.6655621
## X21 -1.520913 -1.5444060
## X22 -1.515956 -2.0406102
## X23 -1.489239 -0.9275957
## X25 -1.338889 -1.3273311
## X26 -1.429814 -1.1365073
## X27 -1.437240 -1.0628195
## X28 -1.510212 -2.1336080
## X29 -1.395077 -1.1516587
## X30 -1.544904 -1.8096966
## X32 -1.396495 -0.8985507
## X33 -1.397561 -1.3662211
## X34 -1.443230 -1.3004918
## X35 -1.467258 -1.0606668
## X36 -1.423188 -0.8679915
## X38 -1.677854 -2.4867416
## X39 -1.694115 -3.0556014
## X40 -1.406135 -1.7749290
## X41 -1.617070 -1.6551407
## X43 -1.548331 -0.9266552
## X45 -1.445488 -1.2910944
## X47 -1.527155 -1.5892127
## X49 -1.448886 -1.8159323
## X50 -1.585739 -1.7326167
## X51 -1.621318 -2.0547020
## X52 -1.619427 -2.1292007
## X53 -1.593905 -1.7898096
## X55 -1.489637 -1.8669460
## X56 -1.547473 -1.4783924
## X57 -1.401122 -1.3628885
## X58 -1.498044 -1.2888687
## X59 -1.652261 -2.0497116
## X60 -1.363097 -1.5245369
## X62 -1.395786 -1.6686442
## X63 -1.395431 -1.7502930
## X64 -1.670976 -1.4910873
## X66 -1.392600 -1.4705280
## X67 -1.428706 -1.7280747
## X68 -1.530085 -2.0824829
## X73 -1.415161 -1.4747157
## X74 -1.480924 -1.9306463
## X75 -1.579449 -1.9088155
## X76 -1.446619 -1.8851434
## X77 -1.465324 -1.8418938
## X78 -1.454964 -1.2655509
## X79 -1.395786 -0.7116307
## X80 -1.530504 -1.7938986
## X81 -1.425391 -1.8055564
## X83 -1.419530 -2.1213029
## X84 -1.488443 -2.1603515
## X85 -1.494430 -1.4406136
## X86 -1.486061 -1.2902036
## X87 -1.523404 -1.6393726
## X88 -1.547473 -1.1798150
## X90 -1.535556 -1.5629177
## X91 -1.607252 -1.9825153
## X92 -1.544049 -1.9559389
## X94 -1.509803 -1.8647794
## X95 -1.427599 -1.7569038
## X96 -1.573211 -1.2928782
## X97 -1.595732 -2.2380872
## X99 -1.473086 -1.7986859
## X100 -1.473086 -1.8362356
## X101 -1.540640 -1.8844106
## X102 -1.415525 -1.6935843
## X105 -1.560451 -1.7622177
## X106 -1.320199 -1.5651813
## X108 -1.575878 -1.6618738
## X110 -1.374774 -1.7602223
## X111 -1.459169 -1.9738585
## X112 -1.531344 -2.2390390
## X113 -1.717446 -2.0841850
## X114 -1.525902 -2.0970190
## X115 -1.366172 -1.6973693
## X117 -1.578108 -2.9206783
## X118 -1.315025 -1.3402996
## X119 -1.322473 -1.4953499
## X120 -1.627498 -0.8624052
## X121 -1.428706 -1.6417852
## X122 -1.445488 -1.7177547
## X124 -1.520499 -1.7209705
## X126 -1.587999 -2.1134503
## X127 -1.457637 -1.3951992
## X128 -1.652261 -1.7522726
## X131 -1.474648 -1.3956885
## X132 -1.429444 -1.7549169
## X133 -1.487251 -1.3903175
## X134 -1.558708 -1.8327119
## X135 -1.459935 -1.5869030
## X137 -1.538094 -2.8336824
## X138 -1.573211 -1.8662234
## X140 -1.498446 -2.3622810
## X141 -1.553935 -1.5892127
## X142 -1.520085 -1.7850558
## X143 -1.480137 -1.9344474
## X144 -1.520913 -1.3571983
## X145 -1.625591 -2.1702883
## X146 -1.475821 -1.8298999
## X148 -1.664214 -1.7450294
## X151 -1.527155 -1.5377457
## X152 -1.398983 -1.4700056
## X153 -1.529246 -1.5874800
## X154 -1.508987 -1.7404419
## X155 -1.454964 -1.2237105
## X156 -1.561324 -1.5845978
## X157 -1.478172 -2.0299327
## X158 -1.726991 -1.9785734
## X159 -1.536401 -1.9888468
## X160 -1.581689 -1.8221988
## X161 -1.502079 -1.5533453
## X163 -1.470361 -1.3136025
## X164 -1.464938 -2.1996053
## X165 -1.556535 -1.3384376
## X166 -1.640502 -1.8880790
## X168 -1.583037 -1.7729134
## X169 -1.494831 -2.3033148
## X170 -1.561761 -2.0790851
## X171 -1.491231 -1.7615522
## X172 -1.484873 -1.7241946
## X173 -1.435005 -1.5267281
## X174 -1.561761 -2.5859017
## X175 -1.625591 -1.8418938
## X176 -1.585739 -1.9283710
## X177 -1.525485 -1.9118051
## X178 -1.479351 -1.6190575
## X179 -1.763600 -2.1748286
## X180 -1.585739 -2.7364649
## X181 -1.457254 -1.7424059
## X182 -1.450022 -1.1242373
## X183 -1.479744 -1.4114596
## X184 -1.604471 -2.6997069
## X186 -1.438733 -1.6929546
## X187 -1.553935 -1.5322240
## X188 -1.516368 -1.9436150
## X189 -1.502888 -1.5355339
## X190 -1.616129 -2.0144782
## X192 -1.694063 -2.2845122
## X193 -1.824755 -2.5774861
## X195 -1.519257 -1.6514837
## X196 -1.615659 -1.6369648
## X198 -1.724360 -2.1091071
## X199 -1.516780 -1.5394073
## X202 -1.492829 -1.6961064
## X203 -1.433147 -1.5366393
## X204 -1.209422 -1.0042088
## X205 -1.475039 -1.6429933
## X206 -1.450022 -1.4224305
## X207 -1.479351 -1.6581966
## X208 -1.609112 -1.4948162
## X210 -1.559143 -2.1522740
## X212 -1.534290 -1.9722906
## X214 -1.550051 -2.9953191
## X215 -1.424656 -0.9098798
## X216 -1.461856 -1.3195307
## X218 -1.686819 -1.7345684
## X219 -1.504104 -1.6096144
## X220 -1.482107 -1.8397690
## X224 -1.427231 -1.1650483
## X225 -1.535978 -1.9952086
## X226 -1.527155 -1.6143267
## X227 -1.508987 -1.9110570
## X229 -1.556969 -1.7622177
## X230 -1.348222 -1.4264463
## X231 -1.373392 -1.5869030
## X232 -1.703821 -1.7470007
## X233 -1.663010 -1.7068831
## X234 -1.558708 -2.0513730
## X235 -1.445111 -1.8090056
## X236 -1.531344 -2.2390390
## X238 -1.556535 -2.2276590
## X240 -1.499252 -1.7443730
## X241 -1.535133 -1.9762139
## X243 -1.506542 -1.4773407
## X244 -1.695111 -1.8756488
## X245 -1.460703 -1.7011661
## X247 -1.613783 -1.8021165
## X248 -1.545331 -1.8932321
## X249 -1.446997 -1.4254410
## X250 -1.489637 -1.8749213
## X251 -1.563950 -1.5771365
## X252 -1.576324 -1.8208036
## X253 -1.370978 -1.8145440
## X254 -1.478958 -1.5702903
## X255 -1.447752 -1.4406136
## X257 -1.533868 -1.7675541
## X258 -1.352253 -1.5039222
## X260 -1.313415 -1.3748365
## X261 -1.438360 -1.5629177
## X264 -1.621791 -1.8611765
## X265 -1.425023 -1.5267281
## X267 -1.563074 -1.6885556
## X268 -1.660208 -2.0439127
## X269 -1.544476 -1.3314830
## X270 -1.511439 -1.9185567
## X271 -1.738456 -2.0340294
## X273 -1.537670 -1.7575668
## X276 -1.501675 -2.2447636
## X277 -1.543196 -1.8083150
## X278 -1.550051 -1.9474538
## X279 -1.638076 -2.1389154
## X280 -1.575433 -1.6791818
## X281 -1.346547 -1.5039222
## X282 -1.644889 -1.5915268
## X283 -1.439855 -1.3379726
## X284 -1.499252 -1.8000570
## X286 -1.643912 -1.9960060
## X287 -1.606788 -2.0282975
## X288 -1.682219 -2.1621529
## X289 -1.648811 -1.6791818
## X290 -1.605860 -1.4990925
## X291 -1.663562 -2.1959068
## X292 -1.520499 -1.6748318
## X293 -1.453060 -1.4401046
## X294 -1.497641 -1.5915268
## X295 -1.526737 -2.1091071
## X296 -1.582138 -1.7642162
## X297 -1.698055 -2.3203498
## X298 -1.597105 -2.4969375
## X299 -1.691083 -1.8954469
## X301 -1.448508 -1.6711155
## X302 -1.638076 -1.8597382
## X303 -1.506542 -1.4847225
## X304 -1.480137 -2.2514717
## X305 -1.593905 -2.2562827
## X306 -1.685886 -1.5114749
## X307 -1.601239 -1.8844106
## X309 -1.713449 -2.2005314
## X310 -1.654735 -2.3571020
## X311 -1.558708 -1.3869129
## X312 -1.657217 -1.9762139
## X313 -1.571438 -1.9497625
## X314 -1.618012 -1.4350269
## X315 -1.506542 -1.5680169
## X316 -1.612379 -2.5679247
## X317 -1.662208 -2.1766489
## X318 -1.471917 -1.7715714
## X319 -1.559579 -1.5719981
## X320 -1.747337 -2.5871077
## X321 -1.484477 -1.9163022
## X322 -1.636142 -1.6184651
## X323 -1.429075 -2.0978789
## X324 -1.412624 -0.6826914
## X325 -1.544476 -1.8771050
## X326 -1.491630 -1.8575837
## X327 -1.533868 -2.3643578
## X329 -1.413348 -1.5903692
## X330 -1.475039 -1.8235956
## X331 -1.471528 -1.6399753
## X332 -1.530924 -1.3351867
## X333 -1.475821 -1.4857809
## X334 -1.583937 -1.7695612
## X335 -1.559143 -1.9574875
## X336 -1.466097 -1.9050882
## X337 -1.594818 -2.0555355
## X338 -1.447374 -1.2973504
## X339 -1.484477 -1.7177547
## X340 -1.440979 -1.9276134
## X341 -1.528409 -1.6196501
## X342 -1.554367 -1.6624877
## X343 -1.478172 -1.4810257
## X344 -1.560888 -1.1446385
## X346 -1.501270 -2.0538690
## X347 -1.530504 -1.7267800
## X348 -1.560015 -1.5869030
## X350 -1.528827 -1.5782813
## X351 -1.664817 -1.8270941
## X352 -1.427968 -1.0696662
## X353 -1.435377 -1.2924320
## X354 -1.388371 -1.8822146
## X355 -1.722066 -1.9405520
## X357 -1.508171 -1.5845978
## X358 -1.589811 -2.1151915
## X359 -1.655231 -2.0538690
## X360 -1.512259 -2.0373158
## X361 -1.699057 -2.2323896
## X362 -1.595732 -1.8947083
## X364 -1.510212 -2.0875956
## X366 -1.514721 -1.9155516
## X367 -1.535133 -1.4969524
## X369 -1.500059 -1.9920239
## X370 -1.528409 -1.8201066
## X371 -1.479351 -0.8795614
## X372 -1.602161 -2.0104407
## X373 -1.572324 -1.8277950
## X374 -1.423555 -1.8568664
## X375 -1.611911 -1.4694835
## X376 -1.553502 -1.5617875
## X377 -1.594361 -1.9245875
## X378 -1.610510 -1.8532856
## X379 -1.536401 -1.4365479
## X380 -1.221525 -1.1031070
## X381 -1.406135 -1.4590896
## X384 -1.474648 -1.7668858
## X385 -1.583937 -1.8235956
## X386 -1.520913 -2.0185279
## X389 -1.613314 -2.3063064
## X390 -1.546616 -1.9405520
## X391 -1.508171 -1.6904389
## X392 -1.461856 -2.0447396
## X393 -1.381022 -1.5427374
## X394 -1.445865 -1.2325409
## X395 -1.472696 -1.6220237
## X396 -1.630368 -1.9817260
## X397 -1.474257 -1.8734676
## X398 -1.686456 -2.4856130
## X399 -1.604934 -1.9298875
## X400 -1.549621 -1.7938986
## X402 -1.495633 -2.0323892
## X404 -1.581241 -1.4831367
## X405 -1.639046 -2.1721026
## X406 -1.500059 -2.2154336
## X407 -1.566145 -1.7945814
## X408 -1.693330 -2.0096347
## X409 -1.453440 -1.6155076
## X410 -1.551343 -1.4025614
## X411 -1.464552 -1.6680271
## X412 -1.497641 -1.6527015
## X413 -1.620845 -2.1030497
## X414 -1.625115 -1.5561527
## X415 -1.592083 -1.5174436
## X416 -1.475039 -1.6066785
## X418 -1.440230 -1.6496593
## X419 -1.520085 -1.7966320
## X421 -1.547473 -1.6303680
## X422 -1.520913 -1.7615522
## X423 -1.479351 -1.7884496
## X424 -1.592538 -1.8180177
## X425 -1.486061 -1.5377457
## X426 -1.602161 -2.1265631
## X427 -1.514721 -1.6393726
## X429 -1.647829 -2.0970190
## X430 -1.629410 -2.2983428
## X431 -1.477780 -1.7358713
## X432 -1.465711 -1.9559389
## X433 -1.386615 -1.6321636
## X435 -1.560888 -1.9801488
## X436 -1.405058 -1.5471923
## X437 -1.562636 -1.4747157
## X438 -1.570996 -1.8313051
## X439 -1.605860 -1.9896404
## X440 -1.645868 -2.3274232
## X441 -1.488841 -1.9683790
## X443 -1.627498 -2.6386361
## X446 -1.521328 -1.9230772
## X447 -1.484873 -1.6686442
## X448 -1.557403 -1.3333333
## X449 -1.619427 -2.0234038
## X450 -1.498044 -2.1996053
## X451 -1.690457 -2.1657627
## X452 -1.436122 -2.1766489
## X453 -1.565266 -2.0430863
## X456 -1.581241 -2.2678960
## X457 -1.482896 -1.7241946
## X458 -1.530504 -2.0455670
## X459 -1.560888 -2.1648594
## X461 -1.409733 -1.6454131
## X464 -1.508579 -1.5300225
## X465 -1.533447 -2.2304954
## X466 -1.568346 -1.7496338
## X467 -1.504916 -1.9505330
## X468 -1.490035 -1.6172812
## X469 -1.565705 -2.1693820
## X470 -1.347217 -1.8778337
## X471 -1.553935 -1.5499851
## X473 -1.630847 -1.8575837
## X474 -1.692284 -2.0748497
## X475 -1.512668 -1.9367333
## X476 -1.515133 -1.6478377
## X477 -1.589811 -1.9730743
## X478 -1.694272 -1.8640580
## X479 -1.504510 -1.6879284
## X480 -1.494430 -1.4720966
## X481 -1.565266 -2.0773893
## X482 -1.631327 -2.1204282
## X483 -1.488046 -1.5207121
## X485 -1.431294 -1.9551652
## X486 -1.579897 -1.5185321
## X487 -1.594818 -2.0364934
## X488 -1.441353 -1.4996282
## X490 -1.658213 -0.9244642
## X491 -1.544476 -1.5921060
## X492 -1.669659 -2.7364649
## X495 -1.605860 -1.8583015
## X496 -1.561761 -2.1091071
## X499 -1.480531 -1.9920239
## X500 -1.460319 -2.0160966
## X503 -1.416979 -1.5606584
## X504 -1.569228 -1.7087947
## X505 -1.307322 -1.6285751
## X506 -1.274705 -1.7476584
## X508 -1.387668 -1.7932162
## X510 -1.363438 -1.6435978
## X511 -1.627020 -1.9193090
## X512 -1.596189 -2.1398020
## X513 -1.419165 -1.3402996
## X514 -1.558708 -1.9028570
## X515 -1.548761 -2.1867032
## X516 -1.453060 -1.6155076
## X517 -1.449644 -1.6072651
## X518 -1.478565 -1.9613670
## X519 -1.463396 -1.9359709
## X520 -1.456108 -1.6090266
## X521 -1.324101 -1.2964545
## X522 -1.508579 -1.5595304
## X523 -1.610510 -1.9551652
## X524 -1.475430 -1.7470007
## X528 -1.529246 -1.5863263
## X529 -1.487648 -2.3033148
## X530 -1.438733 -1.7925342
## X533 -1.541066 -1.7756016
## X535 -1.474257 -2.1802966
## X536 -1.539366 -1.6055062
## X538 -1.351243 -1.7776215
## X539 -1.544476 -1.6166897
## X540 -1.411177 -1.7864122
## X541 -1.507356 -2.1442434
## X542 -1.509395 -1.5427374
## X543 -1.633249 -1.8334159
## X545 -1.541066 -2.2173075
## X547 -1.532184 -1.8626165
## X549 -1.569228 -1.9590379
## X552 -1.620372 -1.5527846
## X553 -1.553935 -2.0765423
## X554 -1.612846 -2.0530365
## X555 -1.556969 -2.1065078
## X558 -1.627020 -2.0201513
## X559 -1.649795 -2.2088944
## X560 -1.526737 -2.3519414
## X561 -1.550912 -2.2163702
## X562 -1.700430 -3.0539870
## X563 -1.478565 -1.1276737
## X564 -1.482501 -1.6954754
## X565 -1.481318 -2.4065265
## X566 -1.583937 -1.9436150
## X567 -1.596189 -2.2466769
## X568 -1.391894 -1.1284389
## X569 -1.714905 -1.7326167
## X570 -1.401837 -0.9485186
## X571 -1.552206 -1.8138504
## X572 -1.468032 -1.3273311
## X573 -1.246824 -0.4547732
## X574 -1.495633 -2.1134503
## X576 -1.468808 -1.6137366
## X577 -1.390483 -1.5377457
## X579 -1.323124 -1.0268307
## X580 -1.577215 -1.6835473
## X582 -1.644401 -1.5488672
## X584 -1.391541 -1.3351867
## X585 -1.382068 -1.0794752
## X586 -1.460319 -1.6339618
## X587 -1.344209 -1.2853171
## X588 -1.442104 -1.8014296
## X591 -1.515956 -2.0406102
## X592 -1.489239 -0.9275957
## X593 -1.484873 -1.7648831
## X594 -1.338889 -1.3273311
## X595 -1.429814 -1.1365073
## X596 -1.437240 -1.0628195
## X597 -1.510212 -2.1336080
## X598 -1.395077 -1.1516587
## X599 -1.544904 -1.8096966
## X600 -1.450022 -1.4079909
## X602 -1.397561 -1.3662211
## X604 -1.467258 -1.0606668
## X605 -1.423188 -0.8679915
## X606 -1.467258 -1.3375078
## X607 -1.677854 -2.4867416
## X608 -1.694115 -3.0556014
## X609 -1.406135 -1.7749290
## X610 -1.617070 -1.6551407
## X612 -1.548331 -0.9266552
## X613 -1.435377 -1.2707870
## X614 -1.445488 -1.2910944
## X615 -1.381719 -1.2448554
## X616 -1.527155 -1.5892127
## X618 -1.448886 -1.8159323
## X620 -1.621318 -2.0547020
## X621 -1.619427 -2.1292007
## X622 -1.593905 -1.7898096
## X623 -1.534290 -1.6387702
## X624 -1.489637 -1.8669460
## X626 -1.401122 -1.3628885
## X627 -1.498044 -1.2888687
## X629 -1.363097 -1.5245369
## X630 -1.536401 -1.3534209
## X631 -1.395786 -1.6686442
## X633 -1.670976 -1.4910873
## X634 -1.323775 -1.4385789
## X635 -1.392600 -1.4705280
## X636 -1.428706 -1.7280747
## X637 -1.530085 -2.0824829
## X638 -1.453440 -1.0758313
## X639 -1.527572 -2.0970190
## X640 -1.571881 -1.9598138
## X641 -1.565705 -2.2126273
## X642 -1.415161 -1.4747157
## X643 -1.480924 -1.9306463
## X644 -1.579449 -1.9088155
## X645 -1.446619 -1.8851434
## X647 -1.454964 -1.2655509
## X648 -1.395786 -0.7116307
## X650 -1.425391 -1.8055564
## X651 -1.433147 -1.3676525
## X652 -1.419530 -2.1213029
## X653 -1.488443 -2.1603515
## X654 -1.494430 -1.4406136
## X655 -1.486061 -1.2902036
## X656 -1.523404 -1.6393726
## X658 -1.524236 -1.6686442
## X659 -1.535556 -1.5629177
## X660 -1.607252 -1.9825153
## X661 -1.544049 -1.9559389
## X663 -1.509803 -1.8647794
## X664 -1.427599 -1.7569038
## X666 -1.595732 -2.2380872
## X667 -1.519257 -2.5478042
## X668 -1.473086 -1.7986859
## X669 -1.473086 -1.8362356
## X670 -1.540640 -1.8844106
## X671 -1.415525 -1.6935843
## X672 -1.603546 -1.8532856
## X673 -1.424656 -1.9058328
## X674 -1.560451 -1.7622177
## X675 -1.320199 -1.5651813
## X676 -1.378587 -1.7756016
## X678 -1.374083 -1.1407587
## X680 -1.459169 -1.9738585
## X681 -1.531344 -2.2390390
## X682 -1.717446 -2.0841850
## X683 -1.525902 -2.0970190
## X684 -1.366172 -1.6973693
## X686 -1.578108 -2.9206783
## X688 -1.322473 -1.4953499
## X690 -1.428706 -1.6417852
## X691 -1.445488 -1.7177547
## X692 -1.375812 -1.5234428
## X693 -1.520499 -1.7209705
## X694 -1.650288 -2.4194174
## X696 -1.457637 -1.3951992
## X697 -1.652261 -1.7522726
## X698 -1.490832 -1.9298875
## X699 -1.536401 -1.4789186
## X700 -1.474648 -1.3956885
## X702 -1.487251 -1.3903175
## X703 -1.558708 -1.8327119
## X705 -1.477780 -1.7602223
## X707 -1.573211 -1.8662234
## X708 -1.480924 -1.4229317
## X710 -1.553935 -1.5892127
## X711 -1.520085 -1.7850558
## X712 -1.480137 -1.9344474
## X713 -1.520913 -1.3571983
## X714 -1.625591 -2.1702883
## X716 -1.491231 -0.6320347
## X717 -1.664214 -1.7450294
## X718 -1.519257 -1.8554329
## X719 -1.677342 -2.1256850
## X720 -1.527155 -1.5377457
## X721 -1.398983 -1.4700056
## X722 -1.529246 -1.5874800
## X723 -1.508987 -1.7404419
## X724 -1.454964 -1.2237105
## X725 -1.561324 -1.5845978
## X727 -1.726991 -1.9785734
## X728 -1.536401 -1.9888468
## X729 -1.581689 -1.8221988
## X730 -1.502079 -1.5533453
## X731 -1.603084 -2.0463949
## X732 -1.470361 -1.3136025
## X733 -1.464938 -2.1996053
## X735 -1.640502 -1.8880790
## X736 -1.471139 -2.3747864
## X737 -1.583037 -1.7729134
## X738 -1.494831 -2.3033148
## X739 -1.561761 -2.0790851
## X740 -1.491231 -1.7615522
## X741 -1.484873 -1.7241946
## X742 -1.435005 -1.5267281
## X743 -1.561761 -2.5859017
## X744 -1.625591 -1.8418938
## X745 -1.585739 -1.9283710
## X746 -1.525485 -1.9118051
## X748 -1.763600 -2.1748286
## X749 -1.585739 -2.7364649
## X750 -1.457254 -1.7424059
## X751 -1.450022 -1.1242373
## X753 -1.604471 -2.6997069
## X754 -1.525485 -1.5494260
## X755 -1.438733 -1.6929546
## X756 -1.553935 -1.5322240
## X757 -1.516368 -1.9436150
## X758 -1.502888 -1.5355339
## X759 -1.616129 -2.0144782
## X760 -1.434262 -0.7826129
## X761 -1.694063 -2.2845122
## X762 -1.824755 -2.5774861
## X764 -1.519257 -1.6514837
## X766 -1.361734 -1.6037499
## X767 -1.724360 -2.1091071
## X768 -1.516780 -1.5394073
## X769 -1.427231 -0.9009890
## X770 -1.473867 -1.8720155
## X771 -1.492829 -1.6961064
## X772 -1.433147 -1.5366393
## X773 -1.209422 -1.0042088
## X774 -1.475039 -1.6429933
## X776 -1.479351 -1.6581966
## X777 -1.609112 -1.4948162
## X778 -1.505728 -1.1128640
## X779 -1.559143 -2.1522740
## X781 -1.534290 -1.9722906
## X782 -1.594818 -2.9266464
## X783 -1.550051 -2.9953191
## X784 -1.424656 -0.9098798
## X785 -1.461856 -1.3195307
## X787 -1.686819 -1.7345684
## X788 -1.504104 -1.6096144
## X789 -1.482107 -1.8397690
## X790 -1.521328 -2.0996003
## X791 -1.494831 -1.6125574
## X793 -1.427231 -1.1650483
## X794 -1.535978 -1.9952086
## X795 -1.527155 -1.6143267
## X797 -1.597563 -1.6798045
## X798 -1.556969 -1.7622177
## X799 -1.348222 -1.4264463
## X800 -1.373392 -1.5869030
## X801 -1.703821 -1.7470007
## X802 -1.663010 -1.7068831
## X803 -1.558708 -2.0513730
## X804 -1.445111 -1.8090056
## X805 -1.531344 -2.2390390
## X806 -1.453821 -1.5903692
## X807 -1.556535 -2.2276590
## X808 -1.623214 -2.6004371
## X810 -1.535133 -1.9762139
## X811 -1.644401 -1.7132666
## X812 -1.506542 -1.4773407
## X814 -1.460703 -1.7011661
## X815 -1.440979 -1.7248404
## X817 -1.545331 -1.8932321
## X819 -1.489637 -1.8749213
## X821 -1.576324 -1.8208036
## X822 -1.370978 -1.8145440
## X824 -1.447752 -1.4406136
## X825 -1.442104 -1.6107907
## X826 -1.533868 -1.7675541
## X827 -1.352253 -1.5039222
## X828 -1.445111 -1.4937496
## X829 -1.313415 -1.3748365
## X830 -1.438360 -1.5629177
## X831 -1.551343 -2.0389620
## X832 -1.598480 -1.6113793
## X833 -1.621791 -1.8611765
## X834 -1.425023 -1.5267281
## X835 -1.484873 -1.7345684
## X836 -1.563074 -1.6885556
## X837 -1.660208 -2.0439127
## X838 -1.544476 -1.3314830
## X839 -1.511439 -1.9185567
## X840 -1.738456 -2.0340294
## X841 -1.502079 -1.8256936
## X842 -1.537670 -1.7575668
## X843 -1.459169 -1.7522726
## X844 -1.519671 -1.9968038
## X845 -1.501675 -2.2447636
## X846 -1.543196 -1.8083150
## X847 -1.550051 -1.9474538
## X848 -1.638076 -2.1389154
## X849 -1.575433 -1.6791818
## X851 -1.644889 -1.5915268
## X852 -1.439855 -1.3379726
## X853 -1.499252 -1.8000570
## X855 -1.643912 -1.9960060
## X856 -1.606788 -2.0282975
## X857 -1.682219 -2.1621529
## X858 -1.648811 -1.6791818
## X859 -1.605860 -1.4990925
## X860 -1.663562 -2.1959068
## X861 -1.520499 -1.6748318
## X862 -1.453060 -1.4401046
## X863 -1.497641 -1.5915268
## X864 -1.526737 -2.1091071
## X866 -1.698055 -2.3203498
## X868 -1.691083 -1.8954469
## X869 -1.594361 -2.2380872
## X871 -1.638076 -1.8597382
## X872 -1.506542 -1.4847225
## X873 -1.480137 -2.2514717
## X874 -1.593905 -2.2562827
## X875 -1.685886 -1.5114749
## X877 -1.669710 -1.6569733
## X878 -1.713449 -2.2005314
## X879 -1.654735 -2.3571020
## X880 -1.558708 -1.3869129
## X881 -1.657217 -1.9762139
## X883 -1.618012 -1.4350269
## X884 -1.506542 -1.5680169
## X885 -1.612379 -2.5679247
## X887 -1.471917 -1.7715714
## X888 -1.559579 -1.5719981
## X889 -1.747337 -2.5871077
## X890 -1.484477 -1.9163022
## X891 -1.636142 -1.6184651
## X892 -1.429075 -2.0978789
## X893 -1.412624 -0.6826914
## X894 -1.544476 -1.8771050
## X897 -1.656223 -2.2923990
## X898 -1.413348 -1.5903692
## X899 -1.475039 -1.8235956
## X900 -1.471528 -1.6399753
## X901 -1.530924 -1.3351867
## X902 -1.475821 -1.4857809
## X903 -1.583937 -1.7695612
## X904 -1.559143 -1.9574875
## X907 -1.447374 -1.2973504
## X908 -1.484477 -1.7177547
## X909 -1.440979 -1.9276134
## X910 -1.528409 -1.6196501
## X911 -1.554367 -1.6624877
## X912 -1.478172 -1.4810257
## X914 -1.461856 -1.8034913
## X915 -1.501270 -2.0538690
## X916 -1.530504 -1.7267800
## X918 -1.435005 -1.7456862
## X920 -1.664817 -1.8270941
## X921 -1.427968 -1.0696662
## X924 -1.722066 -1.9405520
## X926 -1.508171 -1.5845978
## X927 -1.589811 -2.1151915
## X929 -1.512259 -2.0373158
## X930 -1.699057 -2.2323896
## X931 -1.595732 -1.8947083
## X933 -1.510212 -2.0875956
## X934 -1.563074 -1.8201066
## X935 -1.514721 -1.9155516
## X937 -1.498044 -1.5256320
## X938 -1.500059 -1.9920239
## X939 -1.528409 -1.8201066
## X940 -1.479351 -0.8795614
## X942 -1.572324 -1.8277950
## X943 -1.423555 -1.8568664
## X944 -1.611911 -1.4694835
## X945 -1.553502 -1.5617875
## X946 -1.594361 -1.9245875
## X947 -1.610510 -1.8532856
## X948 -1.536401 -1.4365479
## X949 -1.221525 -1.1031070
## X951 -1.616599 -1.5344296
## X952 -1.725620 -2.2727630
## X954 -1.583937 -1.8235956
## X955 -1.520913 -2.0185279
## X957 -1.743107 -1.9668175
## X958 -1.613314 -2.3063064
## X959 -1.546616 -1.9405520
## X960 -1.508171 -1.6904389
## X961 -1.461856 -2.0447396
## X962 -1.381022 -1.5427374
## X963 -1.445865 -1.2325409
## X964 -1.472696 -1.6220237
## X965 -1.630368 -1.9817260
## X966 -1.474257 -1.8734676
## X968 -1.604934 -1.9298875
## X969 -1.549621 -1.7938986
## X970 -1.316639 -1.5109338
## X971 -1.495633 -2.0323892
## X972 -1.697160 -1.5316732
## X975 -1.500059 -2.2154336
## X977 -1.693330 -2.0096347
## X978 -1.453440 -1.6155076
## X979 -1.551343 -1.4025614
## X981 -1.497641 -1.6527015
## X982 -1.620845 -2.1030497
## X983 -1.625115 -1.5561527
## X984 -1.592083 -1.5174436
## X986 -1.436867 -1.7319669
## X988 -1.520085 -1.7966320
## X989 -1.533447 -1.6661779
## X990 -1.547473 -1.6303680
## X991 -1.520913 -1.7615522
## X992 -1.479351 -1.7884496
## X994 -1.486061 -1.5377457
## X995 -1.602161 -2.1265631
## X996 -1.514721 -1.6393726
## X997 -1.524652 -1.6729722
## X998 -1.647829 -2.0970190
## X999 -1.629410 -2.2983428
## X1000 -1.477780 -1.7358713
## X1001 -1.465711 -1.9559389
## X1002 -1.386615 -1.6321636
## X1003 -1.489239 -1.6472311
## X1004 -1.560888 -1.9801488
## X1005 -1.405058 -1.5471923
## X1006 -1.562636 -1.4747157
## X1007 -1.570996 -1.8313051
## X1008 -1.605860 -1.9896404
## X1009 -1.645868 -2.3274232
## X1011 -1.471139 -2.0000000
## X1012 -1.627498 -2.6386361
## X1013 -1.673109 -1.8497148
## X1014 -1.541491 -1.7516124
## X1016 -1.484873 -1.6686442
## X1017 -1.557403 -1.3333333
## X1018 -1.619427 -2.0234038
## X1019 -1.498044 -2.1996053
## X1020 -1.690457 -2.1657627
## X1021 -1.436122 -2.1766489
## X1022 -1.565266 -2.0430863
## X1024 -1.557838 -1.4974871
## X1026 -1.482896 -1.7241946
## X1027 -1.530504 -2.0455670
## X1028 -1.560888 -2.1648594
## X1029 -1.609578 -2.1513794
## X1031 -1.502484 -1.8917577
## X1032 -1.654239 -2.1300810
## X1033 -1.508579 -1.5300225
## X1035 -1.568346 -1.7496338
## X1036 -1.504916 -1.9505330
## X1037 -1.490035 -1.6172812
## X1038 -1.565705 -2.1693820
## X1039 -1.347217 -1.8778337
## X1040 -1.553935 -1.5499851
## X1041 -1.642449 -2.0790851
## X1042 -1.630847 -1.8575837
## X1044 -1.512668 -1.9367333
## X1048 -1.504510 -1.6879284
## X1049 -1.494430 -1.4720966
## X1050 -1.565266 -2.0773893
## X1051 -1.631327 -2.1204282
## X1053 -1.569228 -1.9856773
## X1054 -1.431294 -1.9551652
## X1055 -1.579897 -1.5185321
## X1056 -1.594818 -2.0364934
## X1057 -1.441353 -1.4996282
## X1058 -1.436867 -1.7769479
## X1059 -1.658213 -0.9244642
## X1062 -1.522157 -1.5076925
## X1063 -1.691396 -2.1885391
## X1064 -1.605860 -1.8583015
## X1065 -1.561761 -2.1091071
## X1067 -1.535978 -1.6303680
## X1069 -1.460319 -2.0160966
## X1070 -1.598022 -2.2864798
## X1073 -1.569228 -1.7087947
## X1074 -1.307322 -1.6285751
## X1075 -1.274705 -1.7476584
## X1076 -1.484477 -1.8426028
## X1078 -1.503699 -2.1702883
## X1079 -1.363438 -1.6435978
## X1080 -1.627020 -1.9193090
## X1081 -1.596189 -2.1398020
## X1082 -1.419165 -1.3402996
## X1083 -1.558708 -1.9028570
## X1084 -1.548761 -2.1867032
## X1085 -1.453060 -1.6155076
## X1086 -1.449644 -1.6072651
## X1087 -1.478565 -1.9613670
## X1088 -1.463396 -1.9359709
## X1089 -1.456108 -1.6090266
## X1091 -1.508579 -1.5595304
## X1092 -1.610510 -1.9551652
## X1093 -1.475430 -1.7470007
## X1095 -1.395077 -1.6618738
## X1096 -1.401122 -1.3719574
## X1099 -1.438733 -1.7925342
## X1100 -1.505728 -2.0177170
## X1101 -1.427968 -1.5322240
## X1102 -1.541066 -1.7756016
## X1103 -1.615659 -1.5245369
## X1104 -1.474257 -2.1802966
## X1107 -1.351243 -1.7776215
## X1108 -1.544476 -1.6166897
## X1110 -1.507356 -2.1442434
## X1111 -1.509395 -1.5427374
## X1112 -1.633249 -1.8334159
## X1114 -1.541066 -2.2173075
## X1115 -1.561761 -1.8910211
## X1116 -1.532184 -1.8626165
## X1117 -1.461471 -1.8554329
## X1118 -1.569228 -1.9590379
## X1119 -1.567024 -1.6160985
## X1120 -1.662208 -2.0340294
## X1121 -1.620372 -1.5527846
## X1122 -1.553935 -2.0765423
## X1123 -1.612846 -2.0530365
## X1124 -1.556969 -2.1065078
## X1125 -1.491630 -2.2390390
## X1126 -1.540640 -2.2051713
## X1127 -1.627020 -2.0201513
## X1128 -1.649795 -2.2088944
## X1130 -1.550912 -2.2163702
## X1131 -1.700430 -3.0539870
## X1132 -1.478565 -1.1276737
## X1133 -1.482501 -1.6954754
## X1134 -1.481318 -2.4065265
## X1135 -1.583937 -1.9436150
## X1137 -1.391894 -1.1284389
##
## $usekernel
## [1] FALSE
##
## $varnames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $xNames
## [1] "texture_mean" "smoothness_mean" "compactness_se" "texture_worst"
## [5] "smoothness_worst" "symmetry_worst"
##
## $problemType
## [1] "Classification"
##
## $tuneValue
## fL usekernel adjust
## 1 2 FALSE FALSE
##
## $obsLevels
## [1] "M" "B"
## attr(,"ordered")
## [1] FALSE
##
## $param
## list()
##
## attr(,"class")
## [1] "NaiveBayes"
BAL_NB_Tune$results## usekernel fL adjust ROC Sens Spec ROCSD SensSD
## 1 FALSE 2 FALSE 0.8864212 0.7576471 0.8643356 0.02628655 0.04767519
## 2 TRUE 2 FALSE NaN NaN NaN NA NA
## SpecSD
## 1 0.03462688
## 2 NA
(BAL_NB_Train_AUROC <- BAL_NB_Tune$results[BAL_NB_Tune$results$usekernel==BAL_NB_Tune$bestTune$usekernel &
BAL_NB_Tune$results$adjust==BAL_NB_Tune$bestTune$adjust &
BAL_NB_Tune$results$fL==BAL_NB_Tune$bestTune$fL,
c("ROC")])## [1] 0.8864212
##################################
# Identifying and plotting the
# best model predictors
##################################
# model does not support variable importance measurement
##################################
# Independently evaluating the model
# on the test set
##################################
BAL_NB_Test <- data.frame(BAL_NB_Test_Observed = MA_Test$diagnosis,
BAL_NB_Test_Predicted = predict(BAL_NB_Tune,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
BAL_NB_Test## BAL_NB_Test_Observed BAL_NB_Test_Predicted.M BAL_NB_Test_Predicted.B
## 8 M 9.812673e-01 0.0187326580
## 13 M 7.881578e-01 0.2118422442
## 16 M 9.997184e-01 0.0002815572
## 19 M 8.671011e-01 0.1328989311
## 24 M 7.560913e-01 0.2439087121
## 31 M 9.928321e-01 0.0071679181
## 37 M 9.719732e-01 0.0280268447
## 42 M 9.921992e-01 0.0078007626
## 44 M 9.691234e-01 0.0308765939
## 46 M 9.385286e-01 0.0614713780
## 48 M 9.924481e-01 0.0075519390
## 54 M 6.304132e-01 0.3695868316
## 61 B 2.168351e-02 0.9783164933
## 65 M 9.973897e-01 0.0026103200
## 69 B 9.547752e-01 0.0452247849
## 70 B 1.469355e-03 0.9985306451
## 71 M 1.671223e-01 0.8328777105
## 72 B 2.901232e-03 0.9970987683
## 82 B 7.848737e-01 0.2151262694
## 89 B 6.771435e-01 0.3228564506
## 93 B 1.362549e-04 0.9998637451
## 98 B 3.105533e-01 0.6894466572
## 103 B 1.169672e-02 0.9883032766
## 104 B 7.226036e-01 0.2773964211
## 107 B 9.242559e-01 0.0757441027
## 109 M 9.977118e-01 0.0022882266
## 116 B 7.041220e-01 0.2958779927
## 123 M 9.747772e-01 0.0252228348
## 125 B 1.610990e-03 0.9983890097
## 129 B 1.254316e-01 0.8745684067
## 130 M 9.654620e-01 0.0345379953
## 136 M 6.257974e-01 0.3742025777
## 139 M 7.321426e-01 0.2678574454
## 147 M 9.976976e-01 0.0023024442
## 149 B 3.341167e-02 0.9665883337
## 150 B 2.292809e-03 0.9977071911
## 162 M 3.374330e-03 0.9966256701
## 167 B 1.619263e-06 0.9999983807
## 185 M 5.289888e-01 0.4710111653
## 191 M 9.997987e-01 0.0002013151
## 194 M 9.979348e-01 0.0020651590
## 197 M 9.948181e-01 0.0051819440
## 200 M 9.940340e-01 0.0059659804
## 201 B 5.432820e-01 0.4567179767
## 209 B 9.864943e-01 0.0135057292
## 211 M 5.680945e-01 0.4319054783
## 213 M 1.159697e-01 0.8840303444
## 217 B 8.273550e-01 0.1726450444
## 221 B 9.337968e-04 0.9990662032
## 222 B 1.921628e-02 0.9807837153
## 223 B 2.752034e-01 0.7247966362
## 228 B 7.988226e-03 0.9920117738
## 237 M 9.746734e-01 0.0253266139
## 239 B 5.506258e-01 0.4493741790
## 242 B 5.225347e-05 0.9999477465
## 246 B 7.820348e-01 0.2179652002
## 256 M 5.729148e-01 0.4270851809
## 259 M 9.899523e-01 0.0100476752
## 262 M 1.828597e-01 0.8171403353
## 263 M 5.831366e-01 0.4168633816
## 266 M 9.423911e-01 0.0576089409
## 272 B 7.236743e-04 0.9992763257
## 274 B 1.865623e-02 0.9813437674
## 275 M 4.477037e-01 0.5522963242
## 285 B 1.283059e-03 0.9987169411
## 300 B 1.452065e-01 0.8547934717
## 308 B 7.261562e-06 0.9999927384
## 328 B 2.634175e-04 0.9997365825
## 345 B 2.222892e-02 0.9777710828
## 349 B 1.684737e-02 0.9831526336
## 356 B 4.814841e-02 0.9518515907
## 363 B 2.610626e-01 0.7389373556
## 365 B 4.974638e-03 0.9950253624
## 368 B 2.272191e-01 0.7727808560
## 382 B 4.210317e-03 0.9957896827
## 383 B 1.044271e-02 0.9895572919
## 387 B 9.190923e-04 0.9990809077
## 388 B 6.015690e-05 0.9999398431
## 401 M 9.937431e-01 0.0062568916
## 403 B 1.523278e-02 0.9847672192
## 417 B 8.998595e-01 0.1001405044
## 420 B 3.812498e-01 0.6187501980
## 428 B 6.881346e-01 0.3118654269
## 434 M 9.404447e-01 0.0595552690
## 442 M 8.579651e-01 0.1420348925
## 444 B 4.442142e-03 0.9955578580
## 445 M 3.409643e-02 0.9659035700
## 454 B 3.366251e-03 0.9966337488
## 455 B 1.320695e-02 0.9867930530
## 460 B 1.629774e-01 0.8370226494
## 462 M 9.555680e-01 0.0444320351
## 463 B 2.543201e-02 0.9745679909
## 472 B 2.461675e-01 0.7538324803
## 484 B 1.534474e-02 0.9846552632
## 489 B 1.607416e-01 0.8392584112
## 493 M 7.806461e-01 0.2193538741
## 494 B 2.870437e-06 0.9999971296
## 497 B 7.165893e-01 0.2834107269
## 498 B 7.976398e-02 0.9202360221
## 501 B 1.444296e-02 0.9855570434
## 502 M 9.988572e-01 0.0011427954
## 507 B 6.060617e-01 0.3939382969
## 509 B 3.659993e-03 0.9963400073
## 525 B 8.206683e-02 0.9179331705
## 526 B 3.149118e-02 0.9685088175
## 527 B 8.699125e-01 0.1300875489
## 531 B 3.977364e-01 0.6022636293
## 532 B 8.885794e-01 0.1114206422
## 534 M 4.729801e-01 0.5270199251
## 537 M 9.182135e-01 0.0817865391
## 544 B 3.039975e-01 0.6960024976
## 546 B 4.950886e-01 0.5049114198
## 548 B 1.351934e-01 0.8648065921
## 550 B 4.716409e-01 0.5283590575
## 551 B 3.146255e-03 0.9968537448
## 556 B 8.591647e-01 0.1408352922
## 557 B 1.816539e-01 0.8183460835
## 575 M 9.765204e-01 0.0234796380
## 578 M 9.993858e-01 0.0006142344
## 581 M 9.190702e-01 0.0809297869
## 583 M 3.392679e-01 0.6607320744
## 589 B 2.089111e-02 0.9791088898
## 590 B 1.214756e-01 0.8785244318
## 601 M 9.982402e-01 0.0017598082
## 603 M 9.887012e-01 0.0112988498
## 611 M 9.921992e-01 0.0078007626
## 617 M 9.924481e-01 0.0075519390
## 619 B 2.963917e-01 0.7036083437
## 625 B 6.499918e-02 0.9350008165
## 628 B 1.393959e-03 0.9986060411
## 632 M 9.742588e-01 0.0257411818
## 646 B 2.993435e-04 0.9997006565
## 649 B 2.036427e-01 0.7963572964
## 657 M 9.294722e-01 0.0705278433
## 662 B 1.362549e-04 0.9998637451
## 665 M 9.244672e-01 0.0755327874
## 677 B 4.899191e-02 0.9510080946
## 679 B 7.539674e-01 0.2460325815
## 685 B 7.041220e-01 0.2958779927
## 687 M 9.800610e-01 0.0199390377
## 689 M 4.952652e-01 0.5047347899
## 695 B 5.323889e-03 0.9946761114
## 701 M 6.978591e-01 0.3021408704
## 704 M 8.173221e-01 0.1826778995
## 706 B 1.619121e-01 0.8380878502
## 709 B 6.283883e-03 0.9937161170
## 715 B 4.234854e-02 0.9576514646
## 726 M 7.962994e-01 0.2037006399
## 734 M 7.155837e-01 0.2844163454
## 747 M 8.794790e-01 0.1205210336
## 752 M 9.340052e-01 0.0659947616
## 763 M 9.979348e-01 0.0020651590
## 765 B 5.775093e-03 0.9942249074
## 775 M 1.718460e-01 0.8281540071
## 780 M 5.680945e-01 0.4319054783
## 786 B 8.273550e-01 0.1726450444
## 792 B 2.752034e-01 0.7247966362
## 796 B 6.297508e-03 0.9937024917
## 809 M 9.731131e-01 0.0268868939
## 813 B 5.222499e-02 0.9477750071
## 816 B 7.849621e-03 0.9921503793
## 818 B 9.291527e-01 0.0708472692
## 820 M 8.356248e-01 0.1643752263
## 823 M 3.733182e-01 0.6266817630
## 850 M 9.957456e-01 0.0042544435
## 854 B 1.283059e-03 0.9987169411
## 865 B 1.545147e-04 0.9998454853
## 867 M 1.965801e-02 0.9803419942
## 870 M 8.832716e-01 0.1167284359
## 876 B 7.248908e-04 0.9992751092
## 882 B 7.471474e-04 0.9992528526
## 886 B 3.482215e-06 0.9999965178
## 895 B 3.659113e-02 0.9634088692
## 896 B 4.521132e-04 0.9995478868
## 905 M 8.848879e-01 0.1151120607
## 906 B 1.450107e-03 0.9985498928
## 913 M 9.819396e-01 0.0180604337
## 917 B 3.754428e-03 0.9962455719
## 919 B 5.127009e-02 0.9487299131
## 922 M 9.370673e-01 0.0629327261
## 923 M 9.748852e-01 0.0251147663
## 925 B 4.814841e-02 0.9518515907
## 928 B 8.699658e-04 0.9991300342
## 932 B 2.610626e-01 0.7389373556
## 936 M 9.726773e-01 0.0273226617
## 941 B 5.017463e-05 0.9999498254
## 950 B 1.730492e-01 0.8269507532
## 953 B 5.039053e-01 0.4960947100
## 956 B 9.190923e-04 0.9990809077
## 967 B 3.855968e-03 0.9961440318
## 973 B 6.279003e-02 0.9372099672
## 974 B 7.967346e-05 0.9999203265
## 976 B 4.407911e-03 0.9955920886
## 980 B 3.506288e-01 0.6493711703
## 985 B 8.606275e-01 0.1393724772
## 987 M 9.539449e-01 0.0460550548
## 993 B 1.773299e-01 0.8226700764
## 1010 B 3.395580e-01 0.6604419928
## 1015 B 8.060954e-01 0.1939045871
## 1023 B 3.366251e-03 0.9966337488
## 1025 B 5.667270e-01 0.4332729894
## 1030 M 9.805921e-01 0.0194078603
## 1034 B 9.308926e-03 0.9906910739
## 1043 B 2.092386e-02 0.9790761384
## 1045 B 2.280148e-02 0.9771985228
## 1046 B 2.637147e-01 0.7362852638
## 1047 B 1.747931e-04 0.9998252069
## 1052 B 2.553411e-02 0.9744658931
## 1060 B 3.113604e-01 0.6886396188
## 1061 B 2.238129e-05 0.9999776187
## 1066 B 7.165893e-01 0.2834107269
## 1068 M 3.128620e-01 0.6871380447
## 1071 M 9.988572e-01 0.0011427954
## 1072 B 4.139866e-01 0.5860134296
## 1077 B 5.430217e-01 0.4569782738
## 1090 B 5.656148e-01 0.4343852029
## 1094 B 8.206683e-02 0.9179331705
## 1097 B 6.804212e-04 0.9993195788
## 1098 B 5.610047e-03 0.9943899527
## 1105 M 7.089731e-01 0.2910269275
## 1106 M 9.182135e-01 0.0817865391
## 1109 B 9.474609e-01 0.0525390867
## 1113 B 3.039975e-01 0.6960024976
## 1129 B 8.250674e-01 0.1749326039
## 1136 M 5.683597e-01 0.4316402792
## 1138 B 8.801571e-06 0.9999911984
##################################
# Reporting the independent evaluation results
# for the test set
##################################
BAL_NB_Test_ROC <- roc(response = BAL_NB_Test$BAL_NB_Test_Observed,
predictor = BAL_NB_Test$BAL_NB_Test_Predicted.M,
levels = rev(levels(BAL_NB_Test$BAL_NB_Test_Observed)))
(BAL_NB_Test_AUROC <- auc(BAL_NB_Test_ROC)[1])## [1] 0.9038397
##################################
# Consolidating the base learners
# with optimal hyperparameters
##################################
set.seed(12345678)
BAL_LIST <- caretList(x = MA_Train[,!names(MA_Train) %in% c("diagnosis")],
y = MA_Train$diagnosis,
trControl=RKFold_Control,
metric="ROC",
tuneList=list(
BAL_LDA=caretModelSpec(method="lda",
preProcess=c("center","scale")),
BAL_CART=caretModelSpec(method="rpart",
tuneGrid=data.frame(cp=0.001)),
BAL_SVM_R=caretModelSpec(method="svmRadial",
preProcess=c("center","scale"),
tuneGrid=data.frame(C = 2048, sigma = 0.1790538)),
BAL_KNN=caretModelSpec(method="knn",
preProcess=c("center","scale"),
tuneGrid=data.frame(k = 1)),
BAL_NB=caretModelSpec(method="nb",
tuneGrid=data.frame(usekernel=FALSE,fL = 2,adjust = FALSE)))
)
BAL_LIST## $BAL_LDA
## Linear Discriminant Analysis
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.87416 0.6994118 0.886029
##
##
## $BAL_CART
## CART
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8725597 0.7741176 0.8531411
##
## Tuning parameter 'cp' was held constant at a value of 0.001
##
## $BAL_SVM_R
## Support Vector Machines with Radial Basis Function Kernel
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9081946 0.8058824 0.9272616
##
## Tuning parameter 'sigma' was held constant at a value of 0.1790538
##
## Tuning parameter 'C' was held constant at a value of 2048
##
## $BAL_KNN
## k-Nearest Neighbors
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## Pre-processing: centered (6), scaled (6)
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8981405 0.8882353 0.9080458
##
## Tuning parameter 'k' was held constant at a value of 1
##
## $BAL_NB
## Naive Bayes
##
## 912 samples
## 6 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 730, 730, 730, 729, 729, 729, ...
## Resampling results:
##
## ROC Sens Spec
## 0.8869568 0.7582353 0.8643143
##
## Tuning parameter 'fL' was held constant at a value of 2
## Tuning
## parameter 'usekernel' was held constant at a value of FALSE
## Tuning
## parameter 'adjust' was held constant at a value of FALSE
##
## attr(,"class")
## [1] "caretList"
##################################
# Comparing the base learners
# with optimal hyperparameters
##################################
BAL_LIST_RESAMPLES <- resamples(BAL_LIST)
summary(BAL_LIST_RESAMPLES)##
## Call:
## summary.resamples(object = BAL_LIST_RESAMPLES)
##
## Models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8363171 0.8556266 0.8728070 0.8741600 0.8872549 0.9164962 0
## BAL_CART 0.7928277 0.8517157 0.8767415 0.8725597 0.8908669 0.9225703 0
## BAL_SVM_R 0.8537152 0.8934469 0.9063939 0.9081946 0.9286636 0.9615583 0
## BAL_KNN 0.8582481 0.8863171 0.8900256 0.8981405 0.9080563 0.9472394 0
## BAL_NB 0.8476522 0.8715170 0.8891899 0.8869568 0.9057018 0.9246803 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.6029412 0.6617647 0.7058824 0.6994118 0.7352941 0.8235294 0
## BAL_CART 0.6764706 0.7500000 0.7794118 0.7741176 0.8088235 0.8823529 0
## BAL_SVM_R 0.7205882 0.7794118 0.8088235 0.8058824 0.8235294 0.8823529 0
## BAL_KNN 0.7941176 0.8529412 0.8823529 0.8882353 0.9264706 0.9558824 0
## BAL_NB 0.6764706 0.7205882 0.7352941 0.7582353 0.8088235 0.8529412 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## BAL_LDA 0.8347826 0.8684211 0.8859649 0.8860290 0.9035088 0.9391304 0
## BAL_CART 0.7807018 0.8245614 0.8608696 0.8531411 0.8869565 0.9035088 0
## BAL_SVM_R 0.8508772 0.9122807 0.9298246 0.9272616 0.9473684 0.9736842 0
## BAL_KNN 0.8333333 0.8869565 0.9043478 0.9080458 0.9304348 0.9565217 0
## BAL_NB 0.7982456 0.8421053 0.8596491 0.8643143 0.8859649 0.9304348 0
dotplot(BAL_LIST_RESAMPLES)splom(BAL_LIST_RESAMPLES)##################################
# Measuring the correlation among
# base learners
##################################
BAL_LIST_COR <- modelCor(resamples(BAL_LIST))
##################################
# Formulating an ensemble model
# using the base learners
##################################
set.seed(12345678)
ENL <- caretEnsemble(BAL_LIST,
metric="ROC",
trControl=RKFold_Control)
print(ENL)## A glm ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Generalized Linear Model
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9476578 0.8858824 0.922028
(ENL_Train_AUROC <- ENL$ens_model$results$ROC)## [1] 0.9476578
##################################
# Independently evaluating the model
# on the test set
##################################
ENL_Test <- data.frame(ENL_Test_Observed = MA_Test$diagnosis,
ENL_Test_Predicted = predict(ENL,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
ENL_Test$ENL_Test_Predicted.M <- ENL_Test$ENL_Test_Predicted
ENL_Test## ENL_Test_Observed ENL_Test_Predicted ENL_Test_Predicted.M
## 1 M 0.93698484 0.93698484
## 2 M 0.90316238 0.90316238
## 3 M 0.97601568 0.97601568
## 4 M 0.91077849 0.91077849
## 5 M 0.93437192 0.93437192
## 6 M 0.96912348 0.96912348
## 7 M 0.94640442 0.94640442
## 8 M 0.86974627 0.86974627
## 9 M 0.96390978 0.96390978
## 10 M 0.97013507 0.97013507
## 11 M 0.97048299 0.97048299
## 12 M 0.80221002 0.80221002
## 13 B 0.02124624 0.02124624
## 14 M 0.97377786 0.97377786
## 15 B 0.30541332 0.30541332
## 16 B 0.02099828 0.02099828
## 17 M 0.71594387 0.71594387
## 18 B 0.02056671 0.02056671
## 19 B 0.26152217 0.26152217
## 20 B 0.12122490 0.12122490
## 21 B 0.02050352 0.02050352
## 22 B 0.04257131 0.04257131
## 23 B 0.04396013 0.04396013
## 24 B 0.14459123 0.14459123
## 25 B 0.20488627 0.20488627
## 26 M 0.93524820 0.93524820
## 27 B 0.86514117 0.86514117
## 28 M 0.93285449 0.93285449
## 29 B 0.02085434 0.02085434
## 30 B 0.02510866 0.02510866
## 31 M 0.92653274 0.92653274
## 32 M 0.91582476 0.91582476
## 33 M 0.81037042 0.81037042
## 34 M 0.94862798 0.94862798
## 35 B 0.05990661 0.05990661
## 36 B 0.02129373 0.02129373
## 37 M 0.56743637 0.56743637
## 38 B 0.02059139 0.02059139
## 39 M 0.88679401 0.88679401
## 40 M 0.95930601 0.95930601
## 41 M 0.97588417 0.97588417
## 42 M 0.96579927 0.96579927
## 43 M 0.97560484 0.97560484
## 44 B 0.13604412 0.13604412
## 45 B 0.31905334 0.31905334
## 46 M 0.87024068 0.87024068
## 47 M 0.70472111 0.70472111
## 48 B 0.84716678 0.84716678
## 49 B 0.02517130 0.02517130
## 50 B 0.02453744 0.02453744
## 51 B 0.03493350 0.03493350
## 52 B 0.04507674 0.04507674
## 53 M 0.93284071 0.93284071
## 54 B 0.08406829 0.08406829
## 55 B 0.02052591 0.02052591
## 56 B 0.15755656 0.15755656
## 57 M 0.87275682 0.87275682
## 58 M 0.95026912 0.95026912
## 59 M 0.63082316 0.63082316
## 60 M 0.86809829 0.86809829
## 61 M 0.92185725 0.92185725
## 62 B 0.02052229 0.02052229
## 63 B 0.04423209 0.04423209
## 64 M 0.90013796 0.90013796
## 65 B 0.02649049 0.02649049
## 66 B 0.03040485 0.03040485
## 67 B 0.02051536 0.02051536
## 68 B 0.02050666 0.02050666
## 69 B 0.02126592 0.02126592
## 70 B 0.02614123 0.02614123
## 71 B 0.03134642 0.03134642
## 72 B 0.11493979 0.11493979
## 73 B 0.04325631 0.04325631
## 74 B 0.06191254 0.06191254
## 75 B 0.02227528 0.02227528
## 76 B 0.04325307 0.04325307
## 77 B 0.02322909 0.02322909
## 78 B 0.02052967 0.02052967
## 79 M 0.93484196 0.93484196
## 80 B 0.04345233 0.04345233
## 81 B 0.20634108 0.20634108
## 82 B 0.08299901 0.08299901
## 83 B 0.10948146 0.10948146
## 84 M 0.94731607 0.94731607
## 85 M 0.91087710 0.91087710
## 86 B 0.04080377 0.04080377
## 87 M 0.53269647 0.53269647
## 88 B 0.02061297 0.02061297
## 89 B 0.04383689 0.04383689
## 90 B 0.05612948 0.05612948
## 91 M 0.96382289 0.96382289
## 92 B 0.02332053 0.02332053
## 93 B 0.03470112 0.03470112
## 94 B 0.02244913 0.02244913
## 95 B 0.02845368 0.02845368
## 96 M 0.89267185 0.89267185
## 97 B 0.02049774 0.02049774
## 98 B 0.93276791 0.93276791
## 99 B 0.04884028 0.04884028
## 100 B 0.02392557 0.02392557
## 101 M 0.97541992 0.97541992
## 102 B 0.12656999 0.12656999
## 103 B 0.02364341 0.02364341
## 104 B 0.60219376 0.60219376
## 105 B 0.03038320 0.03038320
## 106 B 0.29505551 0.29505551
## 107 B 0.08107371 0.08107371
## 108 B 0.37623490 0.37623490
## 109 M 0.86105870 0.86105870
## 110 M 0.96287979 0.96287979
## 111 B 0.11154821 0.11154821
## 112 B 0.16941544 0.16941544
## 113 B 0.05579544 0.05579544
## 114 B 0.07386931 0.07386931
## 115 B 0.02282196 0.02282196
## 116 B 0.17065261 0.17065261
## 117 B 0.04008923 0.04008923
## 118 M 0.95269855 0.95269855
## 119 M 0.97534468 0.97534468
## 120 M 0.91998540 0.91998540
## 121 M 0.78809492 0.78809492
## 122 B 0.02246830 0.02246830
## 123 B 0.02679706 0.02679706
## 124 M 0.97562896 0.97562896
## 125 M 0.96156820 0.96156820
## 126 M 0.86974627 0.86974627
## 127 M 0.97048299 0.97048299
## 128 B 0.06944286 0.06944286
## 129 B 0.02713114 0.02713114
## 130 B 0.02072300 0.02072300
## 131 M 0.92567944 0.92567944
## 132 B 0.06590589 0.06590589
## 133 B 0.04786883 0.04786883
## 134 M 0.92226876 0.92226876
## 135 B 0.02050352 0.02050352
## 136 M 0.92166295 0.92166295
## 137 B 0.04646474 0.04646474
## 138 B 0.20432795 0.20432795
## 139 B 0.86514117 0.86514117
## 140 M 0.94355480 0.94355480
## 141 M 0.83853249 0.83853249
## 142 B 0.02496440 0.02496440
## 143 M 0.80604979 0.80604979
## 144 M 0.91402925 0.91402925
## 145 B 0.05577617 0.05577617
## 146 B 0.02069193 0.02069193
## 147 B 0.02241456 0.02241456
## 148 M 0.87189250 0.87189250
## 149 M 0.89732060 0.89732060
## 150 M 0.87656130 0.87656130
## 151 M 0.95600731 0.95600731
## 152 M 0.97588417 0.97588417
## 153 B 0.04331679 0.04331679
## 154 M 0.77608179 0.77608179
## 155 M 0.87024068 0.87024068
## 156 B 0.84716678 0.84716678
## 157 B 0.03493350 0.03493350
## 158 B 0.02072247 0.02072247
## 159 M 0.92552933 0.92552933
## 160 B 0.02877609 0.02877609
## 161 B 0.02304007 0.02304007
## 162 B 0.31638845 0.31638845
## 163 M 0.93016447 0.93016447
## 164 M 0.55664617 0.55664617
## 165 M 0.97513038 0.97513038
## 166 B 0.02649049 0.02649049
## 167 B 0.02050233 0.02050233
## 168 M 0.56291502 0.56291502
## 169 M 0.92497981 0.92497981
## 170 B 0.02158747 0.02158747
## 171 B 0.04490583 0.04490583
## 172 B 0.02049772 0.02049772
## 173 B 0.02259309 0.02259309
## 174 B 0.02134849 0.02134849
## 175 M 0.86089019 0.86089019
## 176 B 0.04511237 0.04511237
## 177 M 0.96967602 0.96967602
## 178 B 0.02078233 0.02078233
## 179 B 0.02470231 0.02470231
## 180 M 0.92219409 0.92219409
## 181 M 0.96567901 0.96567901
## 182 B 0.03134642 0.03134642
## 183 B 0.04490922 0.04490922
## 184 B 0.11493979 0.11493979
## 185 M 0.94184407 0.94184407
## 186 B 0.02049983 0.02049983
## 187 B 0.05675360 0.05675360
## 188 B 0.09446704 0.09446704
## 189 B 0.02322909 0.02322909
## 190 B 0.04266479 0.04266479
## 191 B 0.02298553 0.02298553
## 192 B 0.02050079 0.02050079
## 193 B 0.02120569 0.02120569
## 194 B 0.07527884 0.07527884
## 195 B 0.45197849 0.45197849
## 196 M 0.93061325 0.93061325
## 197 B 0.14664425 0.14664425
## 198 B 0.05185987 0.05185987
## 199 B 0.25198837 0.25198837
## 200 B 0.02061297 0.02061297
## 201 B 0.11863038 0.11863038
## 202 M 0.96913088 0.96913088
## 203 B 0.02082299 0.02082299
## 204 B 0.02419760 0.02419760
## 205 B 0.03477747 0.03477747
## 206 B 0.10145534 0.10145534
## 207 B 0.02109181 0.02109181
## 208 B 0.02138614 0.02138614
## 209 B 0.09539717 0.09539717
## 210 B 0.02050890 0.02050890
## 211 B 0.93276791 0.93276791
## 212 M 0.65252829 0.65252829
## 213 M 0.97541992 0.97541992
## 214 B 0.04558308 0.04558308
## 215 B 0.11872470 0.11872470
## 216 B 0.18484289 0.18484289
## 217 B 0.60219376 0.60219376
## 218 B 0.02052025 0.02052025
## 219 B 0.02051262 0.02051262
## 220 M 0.87701919 0.87701919
## 221 M 0.96287979 0.96287979
## 222 B 0.25378926 0.25378926
## 223 B 0.11154821 0.11154821
## 224 B 0.17287149 0.17287149
## 225 M 0.84597953 0.84597953
## 226 B 0.02427533 0.02427533
#################################
# Reporting the independent evaluation results
# for the test set
#################################
ENL_Test_ROC <- roc(response = ENL_Test$ENL_Test_Observed,
predictor = ENL_Test$ENL_Test_Predicted.M,
levels = rev(levels(ENL_Test$ENL_Test_Observed)))
(ENL_Test_AUROC <- auc(ENL_Test_ROC)[1])## [1] 0.9862508
##################################
# Formulating a stacked model
# using the base learners
# and a linear regression meta-model
##################################
set.seed(12345678)
MEL_LR <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="glm")
print(MEL_LR)## A glm ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Generalized Linear Model
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results:
##
## ROC Sens Spec
## 0.9476578 0.8858824 0.922028
(MEL_LR_Train_AUROC <- MEL_LR$ens_model$results$ROC)## [1] 0.9476578
##################################
# Independently evaluating the model
# on the test set
##################################
MEL_LR_Test <- data.frame(MEL_LR_Test_Observed = MA_Test$diagnosis,
MEL_LR_Test_Predicted = predict(MEL_LR,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
MEL_LR_Test$MEL_LR_Test_Predicted.M <- MEL_LR_Test$MEL_LR_Test_Predicted
MEL_LR_Test## MEL_LR_Test_Observed MEL_LR_Test_Predicted MEL_LR_Test_Predicted.M
## 1 M 0.93698484 0.93698484
## 2 M 0.90316238 0.90316238
## 3 M 0.97601568 0.97601568
## 4 M 0.91077849 0.91077849
## 5 M 0.93437192 0.93437192
## 6 M 0.96912348 0.96912348
## 7 M 0.94640442 0.94640442
## 8 M 0.86974627 0.86974627
## 9 M 0.96390978 0.96390978
## 10 M 0.97013507 0.97013507
## 11 M 0.97048299 0.97048299
## 12 M 0.80221002 0.80221002
## 13 B 0.02124624 0.02124624
## 14 M 0.97377786 0.97377786
## 15 B 0.30541332 0.30541332
## 16 B 0.02099828 0.02099828
## 17 M 0.71594387 0.71594387
## 18 B 0.02056671 0.02056671
## 19 B 0.26152217 0.26152217
## 20 B 0.12122490 0.12122490
## 21 B 0.02050352 0.02050352
## 22 B 0.04257131 0.04257131
## 23 B 0.04396013 0.04396013
## 24 B 0.14459123 0.14459123
## 25 B 0.20488627 0.20488627
## 26 M 0.93524820 0.93524820
## 27 B 0.86514117 0.86514117
## 28 M 0.93285449 0.93285449
## 29 B 0.02085434 0.02085434
## 30 B 0.02510866 0.02510866
## 31 M 0.92653274 0.92653274
## 32 M 0.91582476 0.91582476
## 33 M 0.81037042 0.81037042
## 34 M 0.94862798 0.94862798
## 35 B 0.05990661 0.05990661
## 36 B 0.02129373 0.02129373
## 37 M 0.56743637 0.56743637
## 38 B 0.02059139 0.02059139
## 39 M 0.88679401 0.88679401
## 40 M 0.95930601 0.95930601
## 41 M 0.97588417 0.97588417
## 42 M 0.96579927 0.96579927
## 43 M 0.97560484 0.97560484
## 44 B 0.13604412 0.13604412
## 45 B 0.31905334 0.31905334
## 46 M 0.87024068 0.87024068
## 47 M 0.70472111 0.70472111
## 48 B 0.84716678 0.84716678
## 49 B 0.02517130 0.02517130
## 50 B 0.02453744 0.02453744
## 51 B 0.03493350 0.03493350
## 52 B 0.04507674 0.04507674
## 53 M 0.93284071 0.93284071
## 54 B 0.08406829 0.08406829
## 55 B 0.02052591 0.02052591
## 56 B 0.15755656 0.15755656
## 57 M 0.87275682 0.87275682
## 58 M 0.95026912 0.95026912
## 59 M 0.63082316 0.63082316
## 60 M 0.86809829 0.86809829
## 61 M 0.92185725 0.92185725
## 62 B 0.02052229 0.02052229
## 63 B 0.04423209 0.04423209
## 64 M 0.90013796 0.90013796
## 65 B 0.02649049 0.02649049
## 66 B 0.03040485 0.03040485
## 67 B 0.02051536 0.02051536
## 68 B 0.02050666 0.02050666
## 69 B 0.02126592 0.02126592
## 70 B 0.02614123 0.02614123
## 71 B 0.03134642 0.03134642
## 72 B 0.11493979 0.11493979
## 73 B 0.04325631 0.04325631
## 74 B 0.06191254 0.06191254
## 75 B 0.02227528 0.02227528
## 76 B 0.04325307 0.04325307
## 77 B 0.02322909 0.02322909
## 78 B 0.02052967 0.02052967
## 79 M 0.93484196 0.93484196
## 80 B 0.04345233 0.04345233
## 81 B 0.20634108 0.20634108
## 82 B 0.08299901 0.08299901
## 83 B 0.10948146 0.10948146
## 84 M 0.94731607 0.94731607
## 85 M 0.91087710 0.91087710
## 86 B 0.04080377 0.04080377
## 87 M 0.53269647 0.53269647
## 88 B 0.02061297 0.02061297
## 89 B 0.04383689 0.04383689
## 90 B 0.05612948 0.05612948
## 91 M 0.96382289 0.96382289
## 92 B 0.02332053 0.02332053
## 93 B 0.03470112 0.03470112
## 94 B 0.02244913 0.02244913
## 95 B 0.02845368 0.02845368
## 96 M 0.89267185 0.89267185
## 97 B 0.02049774 0.02049774
## 98 B 0.93276791 0.93276791
## 99 B 0.04884028 0.04884028
## 100 B 0.02392557 0.02392557
## 101 M 0.97541992 0.97541992
## 102 B 0.12656999 0.12656999
## 103 B 0.02364341 0.02364341
## 104 B 0.60219376 0.60219376
## 105 B 0.03038320 0.03038320
## 106 B 0.29505551 0.29505551
## 107 B 0.08107371 0.08107371
## 108 B 0.37623490 0.37623490
## 109 M 0.86105870 0.86105870
## 110 M 0.96287979 0.96287979
## 111 B 0.11154821 0.11154821
## 112 B 0.16941544 0.16941544
## 113 B 0.05579544 0.05579544
## 114 B 0.07386931 0.07386931
## 115 B 0.02282196 0.02282196
## 116 B 0.17065261 0.17065261
## 117 B 0.04008923 0.04008923
## 118 M 0.95269855 0.95269855
## 119 M 0.97534468 0.97534468
## 120 M 0.91998540 0.91998540
## 121 M 0.78809492 0.78809492
## 122 B 0.02246830 0.02246830
## 123 B 0.02679706 0.02679706
## 124 M 0.97562896 0.97562896
## 125 M 0.96156820 0.96156820
## 126 M 0.86974627 0.86974627
## 127 M 0.97048299 0.97048299
## 128 B 0.06944286 0.06944286
## 129 B 0.02713114 0.02713114
## 130 B 0.02072300 0.02072300
## 131 M 0.92567944 0.92567944
## 132 B 0.06590589 0.06590589
## 133 B 0.04786883 0.04786883
## 134 M 0.92226876 0.92226876
## 135 B 0.02050352 0.02050352
## 136 M 0.92166295 0.92166295
## 137 B 0.04646474 0.04646474
## 138 B 0.20432795 0.20432795
## 139 B 0.86514117 0.86514117
## 140 M 0.94355480 0.94355480
## 141 M 0.83853249 0.83853249
## 142 B 0.02496440 0.02496440
## 143 M 0.80604979 0.80604979
## 144 M 0.91402925 0.91402925
## 145 B 0.05577617 0.05577617
## 146 B 0.02069193 0.02069193
## 147 B 0.02241456 0.02241456
## 148 M 0.87189250 0.87189250
## 149 M 0.89732060 0.89732060
## 150 M 0.87656130 0.87656130
## 151 M 0.95600731 0.95600731
## 152 M 0.97588417 0.97588417
## 153 B 0.04331679 0.04331679
## 154 M 0.77608179 0.77608179
## 155 M 0.87024068 0.87024068
## 156 B 0.84716678 0.84716678
## 157 B 0.03493350 0.03493350
## 158 B 0.02072247 0.02072247
## 159 M 0.92552933 0.92552933
## 160 B 0.02877609 0.02877609
## 161 B 0.02304007 0.02304007
## 162 B 0.31638845 0.31638845
## 163 M 0.93016447 0.93016447
## 164 M 0.55664617 0.55664617
## 165 M 0.97513038 0.97513038
## 166 B 0.02649049 0.02649049
## 167 B 0.02050233 0.02050233
## 168 M 0.56291502 0.56291502
## 169 M 0.92497981 0.92497981
## 170 B 0.02158747 0.02158747
## 171 B 0.04490583 0.04490583
## 172 B 0.02049772 0.02049772
## 173 B 0.02259309 0.02259309
## 174 B 0.02134849 0.02134849
## 175 M 0.86089019 0.86089019
## 176 B 0.04511237 0.04511237
## 177 M 0.96967602 0.96967602
## 178 B 0.02078233 0.02078233
## 179 B 0.02470231 0.02470231
## 180 M 0.92219409 0.92219409
## 181 M 0.96567901 0.96567901
## 182 B 0.03134642 0.03134642
## 183 B 0.04490922 0.04490922
## 184 B 0.11493979 0.11493979
## 185 M 0.94184407 0.94184407
## 186 B 0.02049983 0.02049983
## 187 B 0.05675360 0.05675360
## 188 B 0.09446704 0.09446704
## 189 B 0.02322909 0.02322909
## 190 B 0.04266479 0.04266479
## 191 B 0.02298553 0.02298553
## 192 B 0.02050079 0.02050079
## 193 B 0.02120569 0.02120569
## 194 B 0.07527884 0.07527884
## 195 B 0.45197849 0.45197849
## 196 M 0.93061325 0.93061325
## 197 B 0.14664425 0.14664425
## 198 B 0.05185987 0.05185987
## 199 B 0.25198837 0.25198837
## 200 B 0.02061297 0.02061297
## 201 B 0.11863038 0.11863038
## 202 M 0.96913088 0.96913088
## 203 B 0.02082299 0.02082299
## 204 B 0.02419760 0.02419760
## 205 B 0.03477747 0.03477747
## 206 B 0.10145534 0.10145534
## 207 B 0.02109181 0.02109181
## 208 B 0.02138614 0.02138614
## 209 B 0.09539717 0.09539717
## 210 B 0.02050890 0.02050890
## 211 B 0.93276791 0.93276791
## 212 M 0.65252829 0.65252829
## 213 M 0.97541992 0.97541992
## 214 B 0.04558308 0.04558308
## 215 B 0.11872470 0.11872470
## 216 B 0.18484289 0.18484289
## 217 B 0.60219376 0.60219376
## 218 B 0.02052025 0.02052025
## 219 B 0.02051262 0.02051262
## 220 M 0.87701919 0.87701919
## 221 M 0.96287979 0.96287979
## 222 B 0.25378926 0.25378926
## 223 B 0.11154821 0.11154821
## 224 B 0.17287149 0.17287149
## 225 M 0.84597953 0.84597953
## 226 B 0.02427533 0.02427533
#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_LR_Test_ROC <- roc(response = MEL_LR_Test$MEL_LR_Test_Observed,
predictor = MEL_LR_Test$MEL_LR_Test_Predicted.M,
levels = rev(levels(MEL_LR_Test$MEL_LR_Test_Observed)))
(MEL_LR_Test_AUROC <- auc(MEL_LR_Test_ROC)[1])## [1] 0.9862508
##################################
# Formulating a stacked model
# using the base learners
# and a random forest meta-model
##################################
set.seed(12345678)
MEL_RF <- caretStack(BAL_LIST,
metric="ROC",
trControl=RKFold_Control,
method="rf")
print(MEL_RF)## A rf ensemble of 5 base models: BAL_LDA, BAL_CART, BAL_SVM_R, BAL_KNN, BAL_NB
##
## Ensemble results:
## Random Forest
##
## 4560 samples
## 5 predictor
## 2 classes: 'M', 'B'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold, repeated 5 times)
## Summary of sample sizes: 3648, 3648, 3648, 3648, 3648, 3648, ...
## Resampling results across tuning parameters:
##
## mtry ROC Sens Spec
## 2 0.9807114 0.9345882 0.9550350
## 3 0.9807720 0.9335294 0.9555245
## 5 0.9797318 0.9323529 0.9537762
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 3.
(MEL_RF_Train_AUROC <- max(MEL_RF$ens_model$results$ROC))## [1] 0.980772
##################################
# Independently evaluating the model
# on the test set
##################################
MEL_RF_Test <- data.frame(MEL_RF_Test_Observed = MA_Test$diagnosis,
MEL_RF_Test_Predicted = predict(MEL_RF,
MA_Test[,!names(MA_Test) %in% c("diagnosis")],
type = "prob"))
MEL_RF_Test$MEL_RF_Test_Predicted.M <- MEL_RF_Test$MEL_RF_Test_Predicted
MEL_RF_Test## MEL_RF_Test_Observed MEL_RF_Test_Predicted MEL_RF_Test_Predicted.M
## 1 M 1.000 1.000
## 2 M 0.982 0.982
## 3 M 1.000 1.000
## 4 M 0.950 0.950
## 5 M 0.528 0.528
## 6 M 1.000 1.000
## 7 M 0.976 0.976
## 8 M 0.884 0.884
## 9 M 1.000 1.000
## 10 M 0.924 0.924
## 11 M 0.902 0.902
## 12 M 0.950 0.950
## 13 B 0.036 0.036
## 14 M 1.000 1.000
## 15 B 0.350 0.350
## 16 B 0.016 0.016
## 17 M 0.700 0.700
## 18 B 0.024 0.024
## 19 B 0.232 0.232
## 20 B 0.274 0.274
## 21 B 0.000 0.000
## 22 B 0.074 0.074
## 23 B 0.034 0.034
## 24 B 0.298 0.298
## 25 B 0.054 0.054
## 26 M 1.000 1.000
## 27 B 0.738 0.738
## 28 M 0.992 0.992
## 29 B 0.000 0.000
## 30 B 0.024 0.024
## 31 M 0.964 0.964
## 32 M 0.842 0.842
## 33 M 0.666 0.666
## 34 M 0.998 0.998
## 35 B 0.638 0.638
## 36 B 0.012 0.012
## 37 M 0.784 0.784
## 38 B 0.000 0.000
## 39 M 0.900 0.900
## 40 M 1.000 1.000
## 41 M 1.000 1.000
## 42 M 1.000 1.000
## 43 M 0.978 0.978
## 44 B 0.248 0.248
## 45 B 0.266 0.266
## 46 M 0.916 0.916
## 47 M 0.978 0.978
## 48 B 0.592 0.592
## 49 B 0.000 0.000
## 50 B 0.000 0.000
## 51 B 0.012 0.012
## 52 B 0.000 0.000
## 53 M 1.000 1.000
## 54 B 0.304 0.304
## 55 B 0.000 0.000
## 56 B 0.260 0.260
## 57 M 0.542 0.542
## 58 M 1.000 1.000
## 59 M 0.778 0.778
## 60 M 0.930 0.930
## 61 M 0.998 0.998
## 62 B 0.002 0.002
## 63 B 0.000 0.000
## 64 M 0.806 0.806
## 65 B 0.000 0.000
## 66 B 0.162 0.162
## 67 B 0.000 0.000
## 68 B 0.000 0.000
## 69 B 0.004 0.004
## 70 B 0.016 0.016
## 71 B 0.002 0.002
## 72 B 0.142 0.142
## 73 B 0.000 0.000
## 74 B 0.032 0.032
## 75 B 0.002 0.002
## 76 B 0.002 0.002
## 77 B 0.000 0.000
## 78 B 0.000 0.000
## 79 M 1.000 1.000
## 80 B 0.104 0.104
## 81 B 0.002 0.002
## 82 B 0.008 0.008
## 83 B 0.636 0.636
## 84 M 1.000 1.000
## 85 M 0.994 0.994
## 86 B 0.000 0.000
## 87 M 0.958 0.958
## 88 B 0.002 0.002
## 89 B 0.124 0.124
## 90 B 0.044 0.044
## 91 M 0.826 0.826
## 92 B 0.022 0.022
## 93 B 0.004 0.004
## 94 B 0.022 0.022
## 95 B 0.128 0.128
## 96 M 0.982 0.982
## 97 B 0.000 0.000
## 98 B 0.832 0.832
## 99 B 0.008 0.008
## 100 B 0.118 0.118
## 101 M 1.000 1.000
## 102 B 0.020 0.020
## 103 B 0.088 0.088
## 104 B 0.788 0.788
## 105 B 0.002 0.002
## 106 B 0.632 0.632
## 107 B 0.046 0.046
## 108 B 0.738 0.738
## 109 M 0.970 0.970
## 110 M 0.972 0.972
## 111 B 0.376 0.376
## 112 B 0.036 0.036
## 113 B 0.020 0.020
## 114 B 0.268 0.268
## 115 B 0.000 0.000
## 116 B 0.530 0.530
## 117 B 0.062 0.062
## 118 M 0.996 0.996
## 119 M 1.000 1.000
## 120 M 0.914 0.914
## 121 M 0.952 0.952
## 122 B 0.024 0.024
## 123 B 0.000 0.000
## 124 M 1.000 1.000
## 125 M 0.996 0.996
## 126 M 0.884 0.884
## 127 M 0.902 0.902
## 128 B 0.098 0.098
## 129 B 0.014 0.014
## 130 B 0.020 0.020
## 131 M 1.000 1.000
## 132 B 0.008 0.008
## 133 B 0.204 0.204
## 134 M 1.000 1.000
## 135 B 0.000 0.000
## 136 M 1.000 1.000
## 137 B 0.000 0.000
## 138 B 0.200 0.200
## 139 B 0.738 0.738
## 140 M 0.998 0.998
## 141 M 0.964 0.964
## 142 B 0.014 0.014
## 143 M 0.968 0.968
## 144 M 0.974 0.974
## 145 B 0.466 0.466
## 146 B 0.060 0.060
## 147 B 0.004 0.004
## 148 M 0.946 0.946
## 149 M 0.948 0.948
## 150 M 0.934 0.934
## 151 M 0.970 0.970
## 152 M 1.000 1.000
## 153 B 0.000 0.000
## 154 M 0.786 0.786
## 155 M 0.916 0.916
## 156 B 0.592 0.592
## 157 B 0.012 0.012
## 158 B 0.000 0.000
## 159 M 0.994 0.994
## 160 B 0.086 0.086
## 161 B 0.000 0.000
## 162 B 0.600 0.600
## 163 M 0.908 0.908
## 164 M 0.090 0.090
## 165 M 1.000 1.000
## 166 B 0.000 0.000
## 167 B 0.000 0.000
## 168 M 0.666 0.666
## 169 M 0.992 0.992
## 170 B 0.000 0.000
## 171 B 0.000 0.000
## 172 B 0.000 0.000
## 173 B 0.002 0.002
## 174 B 0.000 0.000
## 175 M 0.866 0.866
## 176 B 0.000 0.000
## 177 M 0.942 0.942
## 178 B 0.006 0.006
## 179 B 0.000 0.000
## 180 M 1.000 1.000
## 181 M 0.956 0.956
## 182 B 0.002 0.002
## 183 B 0.000 0.000
## 184 B 0.142 0.142
## 185 M 1.000 1.000
## 186 B 0.000 0.000
## 187 B 0.030 0.030
## 188 B 0.094 0.094
## 189 B 0.000 0.000
## 190 B 0.000 0.000
## 191 B 0.000 0.000
## 192 B 0.000 0.000
## 193 B 0.002 0.002
## 194 B 0.208 0.208
## 195 B 0.790 0.790
## 196 M 1.000 1.000
## 197 B 0.446 0.446
## 198 B 0.028 0.028
## 199 B 0.498 0.498
## 200 B 0.002 0.002
## 201 B 0.160 0.160
## 202 M 0.998 0.998
## 203 B 0.164 0.164
## 204 B 0.006 0.006
## 205 B 0.000 0.000
## 206 B 0.172 0.172
## 207 B 0.000 0.000
## 208 B 0.016 0.016
## 209 B 0.146 0.146
## 210 B 0.000 0.000
## 211 B 0.832 0.832
## 212 M 0.448 0.448
## 213 M 1.000 1.000
## 214 B 0.058 0.058
## 215 B 0.200 0.200
## 216 B 0.216 0.216
## 217 B 0.788 0.788
## 218 B 0.000 0.000
## 219 B 0.000 0.000
## 220 M 0.954 0.954
## 221 M 0.972 0.972
## 222 B 0.202 0.202
## 223 B 0.376 0.376
## 224 B 0.418 0.418
## 225 M 0.932 0.932
## 226 B 0.036 0.036
#################################
# Reporting the independent evaluation results
# for the test set
#################################
MEL_RF_Test_ROC <- roc(response = MEL_RF_Test$MEL_RF_Test_Observed,
predictor = MEL_RF_Test$MEL_RF_Test_Predicted.M,
levels = rev(levels(MEL_RF_Test$MEL_RF_Test_Observed)))
(MEL_RF_Test_AUROC <- auc(MEL_RF_Test_ROC)[1])## [1] 0.9884306
##################################
# Consolidating the resampling results
# for the formulated individual models
##################################
(Consolidated_Resampling <- resamples(list(MBS_AB = MBS_AB_Tune,
MBS_GBM = MBS_GBM_Tune,
MBS_XGB = MBS_XGB_Tune,
MBG_RF = MBG_RF_Tune,
MBG_BTREE = MBG_BTREE_Tune,
BAL_LDA = BAL_LDA_Tune,
BAL_CART = BAL_CART_Tune,
BAL_KNN = BAL_KNN_Tune,
BAL_NB = BAL_NB_Tune)))##
## Call:
## resamples.default(x = list(MBS_AB = MBS_AB_Tune, MBS_GBM =
## = MBG_BTREE_Tune, BAL_LDA = BAL_LDA_Tune, BAL_CART = BAL_CART_Tune, BAL_KNN
## = BAL_KNN_Tune, BAL_NB = BAL_NB_Tune))
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BTREE, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
## Performance metrics: ROC, Sens, Spec
## Time estimates for: everything, final model fit
summary(Consolidated_Resampling)##
## Call:
## summary.resamples(object = Consolidated_Resampling)
##
## Models: MBS_AB, MBS_GBM, MBS_XGB, MBG_RF, MBG_BTREE, BAL_LDA, BAL_CART, BAL_KNN, BAL_NB
## Number of resamples: 25
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.9316305 0.9567775 0.9663683 0.9647554 0.9751032 0.9858101 0
## MBS_GBM 0.9202786 0.9524297 0.9627193 0.9599595 0.9720072 0.9840041 0
## MBS_XGB 0.9176471 0.9510230 0.9667183 0.9589816 0.9720072 0.9847781 0
## MBG_RF 0.9299872 0.9576726 0.9669763 0.9609714 0.9732972 0.9778767 0
## MBG_BTREE 0.9247291 0.9515985 0.9614938 0.9579040 0.9717492 0.9786507 0
## BAL_LDA 0.8184143 0.8556502 0.8810630 0.8736974 0.8914322 0.9135550 0
## BAL_CART 0.8157250 0.8470588 0.8759030 0.8699967 0.8950128 0.9122162 0
## BAL_KNN 0.8355263 0.8916880 0.9033887 0.8999215 0.9180946 0.9486584 0
## BAL_NB 0.8240409 0.8697110 0.8884159 0.8864212 0.9076367 0.9236573 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.7794118 0.8676471 0.9117647 0.9011765 0.9411765 0.9558824 0
## MBS_GBM 0.7352941 0.8823529 0.8970588 0.8970588 0.9264706 0.9705882 0
## MBS_XGB 0.7500000 0.8676471 0.9117647 0.8970588 0.9264706 0.9705882 0
## MBG_RF 0.7941176 0.8529412 0.9117647 0.8970588 0.9411765 0.9705882 0
## MBG_BTREE 0.7794118 0.8676471 0.9117647 0.8976471 0.9264706 0.9558824 0
## BAL_LDA 0.6176471 0.6617647 0.6911765 0.6988235 0.7352941 0.8088235 0
## BAL_CART 0.6617647 0.7205882 0.7647059 0.7600000 0.7941176 0.8823529 0
## BAL_KNN 0.7500000 0.8529412 0.8970588 0.8841176 0.9264706 0.9558824 0
## BAL_NB 0.6617647 0.7205882 0.7500000 0.7576471 0.7941176 0.8676471 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## MBS_AB 0.8947368 0.9210526 0.9385965 0.9398352 0.9649123 0.9739130 0
## MBS_GBM 0.8859649 0.9217391 0.9385965 0.9405370 0.9649123 0.9824561 0
## MBS_XGB 0.9035088 0.9298246 0.9391304 0.9405492 0.9561404 0.9739130 0
## MBG_RF 0.9035088 0.9304348 0.9391304 0.9436949 0.9565217 0.9826087 0
## MBG_BTREE 0.9035088 0.9385965 0.9391304 0.9457818 0.9565217 0.9826087 0
## BAL_LDA 0.8157895 0.8508772 0.8869565 0.8831976 0.9043478 0.9304348 0
## BAL_CART 0.8157895 0.8596491 0.8684211 0.8688787 0.8859649 0.9210526 0
## BAL_KNN 0.8684211 0.8947368 0.9130435 0.9157254 0.9385965 0.9565217 0
## BAL_NB 0.7894737 0.8434783 0.8684211 0.8643356 0.8869565 0.9217391 0
##################################
# Exploring the resampling results
# for the formulated individual models
##################################
bwplot(Consolidated_Resampling,
main = "Model Resampling Performance Comparison (Range)",
ylab = "Model",
pch=16,
cex=2,
layout=c(3,1))##################################
# Consolidating the train and test AUROC
# for the formulated individual models
# together with the ensemble and stacked models
##################################
Model <- c('MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BTREE',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'ENL','MEL_LR','MEL_RF',
'MBS_AB','MBS_GBM','MBS_XGB',
'MBG_RF','MBG_BTREE',
'BAL_LDA','BAL_CART','BAL_SVM_R','BAL_KNN','BAL_NB',
'ENL','MEL_LR','MEL_RF')
Set <- c(rep('Cross-Validation',13),rep('Test',13))
AUROC <- c(MBS_AB_Train_AUROC,MBS_GBM_Train_AUROC,MBS_XGB_Train_AUROC,
MBG_RF_Train_AUROC,MBG_BTREE_Train_AUROC,
BAL_LDA_Train_AUROC,BAL_CART_Train_AUROC,BAL_SVM_R_Train_AUROC,BAL_KNN_Train_AUROC,BAL_NB_Train_AUROC,
ENL_Train_AUROC,MEL_LR_Train_AUROC,MEL_RF_Train_AUROC,
MBS_AB_Test_AUROC,MBS_GBM_Test_AUROC,MBS_XGB_Test_AUROC,
MBG_RF_Test_AUROC,MBG_BTREE_Test_AUROC,
BAL_LDA_Test_AUROC,BAL_CART_Test_AUROC,BAL_SVM_R_Test_AUROC,BAL_KNN_Test_AUROC,BAL_NB_Test_AUROC,
ENL_Test_AUROC,MEL_LR_Test_AUROC,MEL_RF_Test_AUROC)
AUROC_Summary <- as.data.frame(cbind(Model,Set,AUROC))
AUROC_Summary$AUROC <- as.numeric(as.character(AUROC_Summary$AUROC))
AUROC_Summary$Set <- factor(AUROC_Summary$Set,
levels = c("Cross-Validation",
"Test"))
AUROC_Summary$Model <- factor(AUROC_Summary$Model,
levels = c('MBS_AB',
'MBS_GBM',
'MBS_XGB',
'MBG_RF',
'MBG_BTREE',
'BAL_LDA',
'BAL_CART',
'BAL_SVM_R',
'BAL_KNN',
'BAL_NB',
'ENL',
'MEL_LR',
'MEL_RF'))
print(AUROC_Summary, row.names=FALSE)## Model Set AUROC
## MBS_AB Cross-Validation 0.9647554
## MBS_GBM Cross-Validation 0.9599595
## MBS_XGB Cross-Validation 0.9589816
## MBG_RF Cross-Validation 0.9609714
## MBG_BTREE Cross-Validation 0.9579040
## BAL_LDA Cross-Validation 0.8736974
## BAL_CART Cross-Validation 0.8699967
## BAL_SVM_R Cross-Validation 0.9095067
## BAL_KNN Cross-Validation 0.8999215
## BAL_NB Cross-Validation 0.8864212
## ENL Cross-Validation 0.9476578
## MEL_LR Cross-Validation 0.9476578
## MEL_RF Cross-Validation 0.9807720
## MBS_AB Test 0.9936284
## MBS_GBM Test 0.9825620
## MBS_XGB Test 0.9830651
## MBG_RF Test 0.9935446
## MBG_BTREE Test 0.9928739
## BAL_LDA Test 0.8984742
## BAL_CART Test 0.8843478
## BAL_SVM_R Test 0.9159121
## BAL_KNN Test 0.9718310
## BAL_NB Test 0.9038397
## ENL Test 0.9862508
## MEL_LR Test 0.9862508
## MEL_RF Test 0.9884306
(AUROC_Plot <- dotplot(Model ~ AUROC,
data = AUROC_Summary,
groups = Set,
main = "Classification Model Performance Comparison",
ylab = "Model",
xlab = "AUROC",
auto.key = list(adj = 1),
type=c("p", "h"),
origin = 0,
alpha = 0.45,
pch = 16,
cex = 2))